Smart Grids―Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications (Green Energy and Technology) [1st ed. 2024] 303137908X, 9783031379086

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Green Energy and Technology

Alfeu J. Sguarezi Filho Rogério V. Jacomini Carlos E. Capovilla Ivan Roberto Santana Casella   Editors

Smart Grids— Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications

Green Energy and Technology

Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to “green”—i.e. environmentally friendly and sustainable—technologies. While a focus lies on energy and power supply, it also covers “green” solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**. **Indexed in Ei Compendex**.

Alfeu J. Sguarezi Filho · Rogério V. Jacomini · Carlos E. Capovilla · Ivan Roberto Santana Casella Editors

Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications

Editors Alfeu J. Sguarezi Filho Federal University of ABC—UFABC Bangú, Santo André, São Paulo, Brazil Carlos E. Capovilla Federal University of ABC—UFABC Santo André, São Paulo, Brazil

Rogério V. Jacomini Ciência e Tecnologia de São Paulo—IFSP Câmpus Hortolândia Instituto Federal de Educação Hortolândia, Brazil Ivan Roberto Santana Casella Federal University of ABC—UFABC Santo André, São Paulo, Brazil

ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-3-031-37908-6 ISBN 978-3-031-37909-3 (eBook) https://doi.org/10.1007/978-3-031-37909-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Introduction

Contextualization Overview In recent years, at last in two decades, the power grid has been experiencing great transformations in its structure and operation. The Smart Grid (SG) concept is considered an evolution of the traditional energy system, including advances in communication technologies which having become possible the rise of intelligent approaches in the grid. In a complementary way, the SGs also attend the development of the energy generation sources, incorporating the utilization of renewable energy. The SGs are in summary composed of a two-way infrastructure for generation and transmission, distribution, and consumption of energy, working in parallel with robust communication technologies carrying out the monitoring and control tasks of the grid to acquire a significant increase in efficiency and reliability of the power generation, to manage the interaction between power supply and demand avoiding contingencies. Normally, the alternative energy sources connected to the grid are from renewable nature, as tidal, biomass, photovoltaic, and wind systems, and extending along the grid. These sources do a direct connection to modern societal concerns regarding high CO2 emission levels and global warming into a climate-friendly environment. In other way, this raises obstacles for coordination and integration of the different sources, including concerns at communication procedures. Thus, it becomes necessary to use a reliable communication system to guarantee an adequate monitoring and control of all system parameters’ task of interest. For an optimized operation, SGs exploit a complete framework formed by communication networks, data management, and real-time monitoring applications. The use of a modern communication system for controlling and monitoring these systems requires a complex infrastructure for an efficient operation. The SGs management’s main purpose is to supervise the relationship between power energy supply and demand in an efficient manner. This management can avoid grid incidences obtaining a significant enlargement in reliability and stability of stock and energy used.

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In a complementary way, the ancillary services must use the SG facilities to support the grid demands. Thus, renewable sources connected to SGs are of interest for these services, where the SG can be a useful tool to ensure the power system stability, for instance, managing the injection of reactive power.

Scope of the Book As aforementioned, SGs use a modern communication infrastructure to implement the management of generation, transmission, and energy distribution in a much more efficient manner. In this context, the scope of this book is to show and disclose different proposal, element, and implementations about SGs, looking for well-defined point of view about some aspects which can cover the SGs operation as a whole. For it, renewable energy, power electronic, signal processing, and communication systems are elected ones to reach this goal. Thus, it is proposed as a book composed of an interconnected collection of chapters which will be very useful for technical students and professionals, whose lines of interest are in connection with SGs. For an organized and logical sequence of the proposed technical chapters, the book is divided into three main parts. • Part I—Renewable Energy Sources and Systems; • Part II—Power Electronics Analysis and Control; • Part III—Signal Processing and Communication Systems.

Part I—Renewable Energy Sources and Systems The main objective of this part is to present renewable energy and systems employed in SG environment. The chapters will describe the energy harvesting in SG, renewable energy sources such as hydrogen production and storage applications. Regarding the power systems in SG, a review of islanding detection methods for distributed generation systems; energy management and virtual plants considering renewable energy systems are presented. The Part Renewable Energy Sources and Systems is composed of the following chapters: • “Energy Harvesting Towards Power Autonomous Sensors in Smart Grids”: This chapter will survey the leading energy harvesting techniques for wireless power sensors in a smart grid scenario. It focuses on mechanical, Radio Frequency, and solar sources, presenting their basic topologies, working principles, capabilities, and main characteristics.

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• “Off-Grid Green Hydrogen Production Systems”: This chapter introduces the role of hydrogen in the current energy system transition, from fossil-based to renewable and low-carbon emission sources. Although solar and wind energies are abundant renewable sources, the intermittence of electricity generation remains a challenge for security of supply and causes instabilities in the electricity grid. The chapter concludes by showing the capabilities of an off-grid water electrolyzer system, which consists of a battery energy system and solar photovoltaic and wind power installations. • “Energy Storage Applications in Renewable Energy Systems”: This chapter’s purpose of surveying is to identify and map/chart the technologies for energy storage systems available and qualitatively evaluate their applicability in the power system considering its impact on the quality and reliability of electric power system. • “A Review of Islanding Detection Methods for Biogas-Based Distributed Generation Systems”: In this chapter, a review over the techniques applied for islanding detection with synchronous machines is done, as well as an analysis of the requirements of the Brazilian standards. • “Distributed Energy Resources Management Systems (DERMS)”: This chapter proposes the description of the Distributed Energy Resources Management Systems (DERMS), as well as the motivation, main concepts, and drivers for the comprehension and further studies on DERMS. • “Virtual Power Plants for Smart Grids Containing Renewable Energy”: This chapter aims to present the optimal models of Virtual Power Plants and the challenges and solutions to their implementation.

Part II—Power Electronics Analysis and Control The main objective of this part is to present power electronics analysis and control that can be employed in SG environment. The chapters will describe high-performance control methods for photovoltaic systems and electrical machines such as permanent magnet synchronous, squirrel cage induction, and doubly-fed induction generators for wind energy systems. In all cases, the cited systems are connected to the electrical grid. This Part Power Electronics Analysis and Control is composed of the following chapters: • “Inverter-Based Local Control Methods for Mitigating Overvoltage in Photovoltaic Penetrated Low-Voltage Networks”: In this chapter, four local voltage control methods using PV inverters are presented to mitigate the voltage rise caused by the growing installation of the PV system in LV networks. Three of these methods use one smart control functionality of PV inverters, while the other

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uses two smart control functionalities in a coordinated way. Power flow simulations are performed using data from a Brazilian real-world and extensive LV network to compare the performance of these control methods. • “Variable-Speed-Driven Three-Phase Surface-Mounted Permanent Magnet Synchronous Machine Applied to Wind Generation Systems”: This chapter aims to develop a theoretical base for applying surface-mounted permanent magnet synchronous generators in wind power generation systems, and thereafter applies this theory to a power generation system tested with computational simulation. By the end of this chapter, the reader should be able to understand the generators’ modeling and the implications of choosing the reference frame and the back-EMF waveform on the control of the machine and its operation. • “Variable Speed Drives for Household Wind Energy Systems: Model Predictive Control of the Squirrel Cage Induction Generator with the Nine-Switch Converter”: This chapter develops the theoretical concepts regarding Nine-Switch Converter (NSC) application in household wind generation with squirrel cage induction generator. Four Predictive Current Control techniques are discussed and compared based on simulation results. The capability to supply reactive power by the NSC during active power generation is also studied. • “Electromagnetic Analysis of a DFIG‘s Controlled Operation Using Finite Elements Method”: This chapter proposes an electromagnetic analysis using the finite element method for the Doubly-Fed Induction Generator (DFIG)based Wind Energy System during its vector-controlled operation by means of a proportional-integral (PI) controller. Moreover, a new design method for the PI controller gains is presented, based on the fact there is no guarantee that the DFIG will operate in the unsaturated condition.

Part III—Signal Processing and Communication Systems The main objective of this part is to present some aspects of signal processing and communication systems at an SG environment. The initial chapters will describe some aspects of signal processing in SGs, with an active filter implementation based on deep neural network. After, two last chapters with experimental wireless communication system will be present showing different aspects at control and at smart city implementation. This Part Signal Processing and Communication Systems is composed of the following chapters: • “Signal Processing Technologies Applied in Smart Grids”: This chapter presents innovative solutions based on different signal processing techniques for fault detection, classification, and location in power systems. The presented methods are based on Euclidean Distance and Independent Components Analysis, proving that more accurate and fast solutions can be reached when using the resources available in modern power grids, implementing smart grid concepts. The presented

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methods were tested against a system with real characteristics and then compared with conventional methods. • “Active Power Filters Applied to Smart Grids: Harmonic Content Estimation Based on Deep Neural Network”: This chapter presents the principles of synthesizing control references for an Active Power Filter (APF), which is placed in a smart grid comprising distortion loads, aiming at achieving power quality enhancement and compliance with standardized indexes. In addition, it is argued that the APF control system requires harmonic content identification to generate the targeted compensating currents. Thus, to achieve the disturbance recognition expected for synthesizing control references, harmonic analysis methods can be devised by automation tools and Artificial Intelligence (AI). • “Short Message Service System Applied in Predictive Control of Inverters Connected to the Electric Grid in Smart Grids Environments”: This chapter proposes a system to remotely control the active and reactive power injected into the electrical grid based on the Short Message Service (SMS), due to its simplicity and low-cost characteristics, inherent to the wireless communication systems. In the proposal, the power references are sent via SMS by the SG operator to the renewable energy source under control, with the obtained results validated on an experimental test-bench. • “Wireless Sensor Network for Environmental Monitoring in Smart Cities”: This chapter presents a wireless sensor network as a solution for environment monitoring in smart cities that combines the usage of two Internet of things solutions, one for measuring toxic gases, temperature, humidity, UV index, and fire presence, and the other for monitoring the garbage collection by tracking the trucks and street sweepers’ cart movements to guarantee its coverage throughout the city. The two solutions have shown effectiveness on obtaining this information dealing with Internet connection oscillations, making the data available through a web application where the data can be exported.

Contents

Renewable Energy Sources and Systems Energy Harvesting Towards Power Autonomous Sensors in Smart Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eduardo V. Valdés Cambero, Vinícius S. Silva, Humberto P. Paz, Renan Trevisoli, Carlos E. Capovilla, and Ivan R. S. Casella

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Off-Grid Green Hydrogen Production Systems . . . . . . . . . . . . . . . . . . . . . . . Alejandro Ibáñez-Rioja, Georgios Sakas, Lauri Järvinen, and Pietari Puranen

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Energy Storage Applications in Renewable Energy Systems . . . . . . . . . . . Fabiano Salvadori, Oswaldo Hideo Ando Junior, Maurício de Campos, Paulo Sérgio Sausen, Eder Andrade da Silva, André Quites Ordovás Santos, and Fernando Marcos de Oliveira

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A Review of Islanding Detection Methods for Biogas-Based Distributed Generation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Alcedir Luis Finkler, Maurício de Campos, Paulo Sérgio Sausen, Fabiano Salvadori, and Airam Teresa Z. R. Sausen Distributed Energy Resources Management Systems (DERMS) . . . . . . . . 143 Alvaro R. Albertini, Vítor T. Yabe, and Silvio G. Di Santo Virtual Power Plants for Smart Grids Containing Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Seyed Iman Taheri, Daniela Wolter Ferreira Touma, and Mauricio Barbosa Camargo de Salles Power Electronics Analysis and Control Inverter-Based Local Control Methods for Mitigating Overvoltage in Photovoltaic Penetrated Low-Voltage Networks . . . . . . . . . . . . . . . . . . . . 197 J. Villavicencio, J. D. Melo, J. B. Leite, and A. Padilha-Feltrin

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Variable-Speed-Driven Three-Phase Surface-Mounted Permanent Magnet Synchronous Machine Applied to Wind Generation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 José Roberto Boffino de Almeida Monteiro and Stefan Thiago Cury Alves dos Santos Variable Speed Drives for Household Wind Energy Systems: Model Predictive Control of the Squirrel Cage Induction Generator with the Nine-Switch Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Paulo Roberto Ubaldo Guazzelli, Stefan Thiago Cury Alves dos Santos, and Manoel Luís de Aguiar Electromagnetic Analysis of a DFIG‘s Controlled Operation Using Finite Elements Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 André L. L. F. Murari, J. S. Solís-Chaves, Ademir Pelizari, Alfeu J. Sguarezi Filho, Bruno H. P. da Silva, and Renato M. Monaro Signal Processing and Communication Systems Signal Processing Technologies Applied in Smart Grids . . . . . . . . . . . . . . . 299 Guilherme Torres de Alencar, Ricardo Caneloi dos Santos, and Aline Neves Active Power Filters Applied to Smart Grids: Harmonic Content Estimation Based on Deep Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Claudionor Francisco do Nascimento, Alfeu Joãozinho Sguarezi Filho, Amilcar Flamarion Querubini Gonçalves, Augusto Matheus dos Santos Alonso, Luiz Gustavo Reis Bernardino, Paulo Fernando Silva, and Wesley Angelino de Souza Short Message Service System Applied in Predictive Control of Inverters Connected to the Electric Grid in Smart Grids Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Angelo Lunardi, Alfeu J. Sguarezi Filho, Carlos E. Capovilla, and Ivan R. S. Casella Wireless Sensor Network for Environmental Monitoring in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Bruno Sousa Dias, Cicero Matheus da Silva Lacerda, Nailson Martins Dantas Landim, Humberto Xavier Araujo, and Starley do Nascimento Lobo

About the Editors

Prof. Dr. Alfeu J. Sguarezi Filho was born in Cuiaba-MT-Brazil, in 1981. He received his Ph.D. degree from Campinas University in SP—Brazil in 2010. He is an IEEE Senior member, and he published a book, and several papers in conferences and journals. Since 2012, he has been a full-time Professor at Federal University of ABC—UFABC, in Santo André—SP—Brazil, teaching in the areas of Electrical Machines, Power Electronics, and Electrical Drives. Prof. Dr. Rogério V. Jacomini was born in Santa Barbara D’ Oeste—SP—Brazil in 1979. He received his M.S. and doctorate degrees from State University of Campinas, Brazil, in 2008 and 2012, respectively. Currently, he is a Professor at Sao Paulo Federal Institute of Education, Science and Technology—IFSP, Hortolândia, Brazil. His research interests are machine drives, doubly-fed induction generators, power control, and electrical power systems. Prof. Dr. Carlos E. Capovilla was born in Vinhedo, Brazil, on March 06, 1977. He received the B.S. degree from the University of Sao Paulo (USP São Carlos) in 2001, M.Sc. and Ph.D. degrees from the University of Campinas in 2004 and 2008, all in Electrical Engineering. He is currently a Professor with the Federal University of ABC, IEEE Senior Member, and published more than one hundred papers in technical journals and conferences. His current research interests include microwave circuits, planar antennas design, energy harvesting applications, and mobile systems design for smart grids. Prof. Dr. Ivan Roberto Santana Casella is full Professor at Federal University of ABC and Founder and Chair of the Information and Communication Laboratories. He holds a master’s and doctorate in Electrical Engineering from the Polytechnic School of the University of Sao Paulo, with a doctoral internship at the Wireless Communication Laboratory of the University of Toronto. He is senior member of the IEEE, Associate Editor of IET Electronic Letters and author of several articles and

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book chapters in telecommunications and smart grid areas, which have resulted in some academic and scientific awards. Additionally, he is the main Editor of the book Power Line Communication Systems for Smart Grids. His main areas of interest are wireless communications, power line communications, smart grids, planar antennas design, and energy harvesting.

Renewable Energy Sources and Systems

Energy Harvesting Towards Power Autonomous Sensors in Smart Grids Eduardo V. Valdés Cambero, Vinícius S. Silva, Humberto P. Paz, Renan Trevisoli, Carlos E. Capovilla, and Ivan R. S. Casella

Abstract Wireless sensors have gained popularity in smart grids scenario, and their increasing number, together with efficient data processing and decision-making, can optimize the entire operation of electricity generation and distribution. In order to meet the sensor energy demand, low-power energy harvesting systems have become a promising alternative since their principle is converting energy available in the surrounding environment into useful electrical energy to supply low-power devices. The advantage of this technique is the abundance and availability of ambient energy sources that, despite having low energy density, are sufficient to individually power each intended sensor continuously, presenting similar versatility to batteries but without constant replacement. In this context, this chapter intends to survey the leading energy harvesting techniques for wireless power sensors in a smart grid scenario, focusing on mechanical, Radio Frequency (RF), and solar sources, presenting their basic topologies, working principles, capabilities, and main characteristics.

E. V. Valdés Cambero Institute of Embedded Systems (InES) at the Applied Science University of Winterthur, Technikumstrasse 9, Wintethur, Switzerland e-mail: [email protected] V. S. Silva (B) · H. P. Paz · R. Trevisoli · C. E. Capovilla · I. R. S. Casella Federal University of ABC, Ave. Dos Estados, 5001, Santo André, SP 09210-580, Brazil e-mail: [email protected] H. P. Paz e-mail: [email protected] R. Trevisoli e-mail: [email protected] C. E. Capovilla e-mail: [email protected] I. R. S. Casella e-mail: [email protected] R. Trevisoli Pontifícia Universidade Católica de São Paulo, São Paulo 01303-050, Brazil © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_1

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1 Introduction The smart cities paradigm can be seen as a strategy to increase the development of industry, public management and also contribute to increasing energy efficiency. Nowadays, the desire for an intelligent society with Wireless Sensor Networks (WSN), Internet of Things (IoT), smart grids, and smart skins, among others, is continually growing. IoT is becoming popular not only due to the emergence of disruptive technologies; but also due to the evolution of available technologies into more accessible solutions, enabling mass adoption. The smart control and monitoring of processes, enabling bi-directional communication and an early diagnosis of possible failure or maintenance, is already adopted as the strategy to speed up the evolution of traditional power grids into smart grid generation. The rapid assimilation of new technologies can generate many devices connected to the cloud in the power grid environment. Sensors can collect information about the power line conditions such as temperatures, defects on the conductor, voltage, current, humidity, or atmospheric pressure. The information recorded is typically sent to a control center in the cloud to be processed. However, the trend in the state-of-the-art (SoA) is to improve data processing at the sensors level to reduce the energy consumption in data transmission/reception routines. Regardless of these solutions are showing a continuous trend to lower the required power, still ensuring the energy required for its operation brings some technical or financial challenges due to its number and locations with difficult or risky access. Batteries dominate the energy suppliers and storage market in low-power applications due to their sizes, satisfactory cost-benefits, efficiency, and well-established manufacturing industry. However, they are characterized by the need for replacement and the high environmental pollution in the waste. Once batteries reach the end of their lifespan, they need to be replaced, becoming waste with a high environmental cost due to the nature of their component materials. Therefore, the battery operating time is a drawback that affects the cost of maintenance (related to replacement) and the logistics for the waste treatment, having the conditioning that the greater the number of devices, the higher the logistic and maintenance cost in the solution [1]. An approach to revert these inconveniences is to research and develop technologies for energy scavenging from surrounding environment sources as an alternative to reduce or even eliminate battery dependence. Low-power energy harvesting is a process through which surrounding energy of different natures is converted into electrical power that can be conditioned to energize ultra-low-power devices. The energy sources more commonly exploited are solar, thermal, mechanical (vibration or acoustics), and electromagnetic (EM) waves. A more comprehensive concept can include some variation of these primary sources, like a hybrid integration of two or more of them or the intended generation of a particular source and its transmission to an optimized receptor, like the case of Wireless Power Transmission (WPT).

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While analyzing the main energy harvesting sources applied in smart grid environments, a particular characteristic is observed. The grid parameter nature to be controlled or sensed determines the location of the low-power device to be energized. Consequently, the final site has an important role in deciding which energy source can be employed. For example, specific power line conditions are measured with sensors attached directly to high-power conductors, which makes the EM energy radiated by the conductor suitable as the energy source. Another scenario can be found in indoor substations where solar energy or another light source might be insufficient. Here, RF or vibration energy might be considered. In short, positioning sensors or actuators also sets constraints and requirements for the energy harvesting source. Objectives of the Chapter This chapter aims to present different energy collection mechanisms that have been proposed in the literature to support power to wireless sensors and actuators operating within power grid ecosystems. For this, in Sect. 2, we start by presenting a general topology of self-powered devices and the role played by the energy harvesting system (the energy source) in smart grid devices. In Sect. 3, the SoA of the energy scavenging process applied to modules for control and monitoring functions in smart grid scenarios is analyzed. Several methods for harvesting surrounding energy in smart grid environments proposed in the literature are presented, detailing their working principles, capabilities, and main characteristics. Finally, Sect. 4, concludes this chapter.

2 Self-powered Devices Energy-autonomous systems collect the operating power by surrounding energy scavenging. The possibility of designing long-term and maintenance/replacement-free devices by leveraging energy-harvesting technologies generates new applications for embedded systems. Energy-autonomous systems, also known in the literature as Autarkic systems, are limited in power budget with functionality and lifetime determined by the utilization and the power available, either in terms of energy stored or harvested from the environment. Based on the above principle, a complete system approach, considering three fundamental aspects as energy harvesting, energy management, and energy consumed by the application, becomes necessary and demonstrates that the system must be designed for energy harvesting. A low-power wireless device with energy scavenging capabilities can be typically sketched as shown in Fig. 1. The diagram shows that different energy sources can be used to energize the device, including the possibility of complementary hybrid approaches. The Power Management Unit (PMU) might include a DC-DC converter, checking routines of the stored energy level, or maximum power tracking algorithms depending on the energy source driving the system and the management strategy. PMUs can optimize the level of collected power by managing the load duty cycles, controlling

6 Fig. 1 Block diagram of a generic low-power wireless sensor/actuator device integrating energy harvesting capabilities

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Hybrid

the charging times, and regulating the microcontroller’s operating modes. Supercapacitors and batteries are the typical storage devices employed in low-power energy harvesting systems. Supercapacitors are electrochemical double-layer capacitors that appear more commonly in some applications and projects because they have shorter charging times and higher charge and discharge cycles. However, batteries possess a settled technology and a higher energy density, allowing more capacity for a given space occupied by the circuit components. The Microcontroller Unit (MCU) performs the interface of the sensor or actuator and the communication block routines. Together with the PMU, it can also be used to manage the energy within the system. MCU has three modes of operation: Power up, Sleep, and Active. In Power-up mode, the MCU waits for the voltage levels to exceed the start-up values and thus starts its operation. In sleep mode, it waits for an external interrupt to arrive from the sensor to be serviced or from the storage system in case it is being used as a power manager. Finally, in Active mode, it is listening for an interruption.

3 Energy Harvesting Techniques for Self-powered Devices in Smart Grid Environment Within the group of power sources available in the surrounding environment, solar energy stands by its superiority in terms of power density, availability, and the suitability of a Solar Cell (SC) as a long-term power source. Different SC technologies have recently attracted considerable attention as a source for low-power consumption electronic products due to the rapid growth of the IoT, which requires covering the power demand of a high number of wireless powered/connected terminals. Besides, the production levels of SC panels have experienced a high increase in the last 20 years, reaching both outdoor and indoor applications. Its fabrication technology is

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well-instituted, and the main raw material is silicon, which also enjoys significant development thanks to the microelectronic industry. The previous elements place solar energy as a potential candidate to complement or substitute the energy from batteries in low-power applications. In the literature, the main efforts related to SC for low-power applications are directed to obtain photovoltaic materials with an improved power conversion efficiency and smaller/adaptable form factor, especially under low irradiance levels and optimized for more common wavelengths of indoor light sources [2]. However, the SC as an energy source has the disadvantage that its energy production varies proportionally with the values of the available power density coming from the light sources. In the case of sunlight, the power density depends on geographical location and the sun’s position during daylight. Low power densities can also be produced by shadowing effects or night periods in outdoor environments or due to the absence of natural or artificial light indoors. These problems cause the power supplied to the device to drop, and its operation can be compromised. From the system design and integration point of view, the ongoing research of solar or hybrid-powered sensors (including solar energy) is focused on maximum power point tracking (MPPT) algorithms and adaptive policies for activation, communication, and power management [3]. EM energy is another power source to be considered nowadays. The energy transmission process from the source to the receptor can be classified into two groups according to the nature of the power sources: • Wireless Power Transfer (WPT) with dedicated beacons or, • Undedicated EM Energy Harvesters (EMEH). Unlike EMEH, WPT classification is used for applications in which the energy of a dedicated beacon is strategically directed to the receptor. At present, the outdoor and indoor environments have the necessary infrastructure to operate various RF systems. These systems transmit RF signals in several frequency bands and with different power intensities. The signals of services such as WiFi (Wireless Fidelity), Wireless Local Area Networks (WLAN), ZigBee, Bluetooth, GSM (Global System for Mobile communications), and LTE (Long-Term Evolution) can be easily found relatively ubiquitously spread out. Specifically in grid environments for high power generation and transmission, a considerable among of radiated power can be found in the vicinity of the conductors. Collecting/receiving EM energy and converting it into an exploitable DC level can be done by using a combination of an antenna and a rectifier circuit (rectenna) or by converting the EM waves into mechanical energy by placing an intermediate power transducer. The resulting mechanical energy can then be re-converted to the desired DC level. Mechanical energy harvesting devices for low-power solutions can produce electricity from induced mechanical movements varying in intensity and periodicity. The origin of the mechanical perturbation dictates the transducer’s nature to be used, which can be divided into three well-established transduction mechanisms: piezoelectric, electromagnetic, and electrostatic generators. More recent efforts have demonstrated the possibility of extracting energy from the dislocation of parallel

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capacitors plates using electret materials as the dielectric between the plates [4] or harnessing the triboelectric effect as energy source [5].

3.1 SoA of Energy Harvesting Solutions Applied to Smart Grid When studying possibilities of energy harvesting to power up sensors or actuators for controlling or monitoring grid systems, a priming idea comes to the table: collecting the energy radiated by the grid itself. In addition, the source of power is relatively constant, only interrupted in case of absence of the power in the line [6]. Several techniques have been proposed to take advantage of the considerable power present in the surrounding areas of the grid conductors. These different approaches can be divided into two major groups: clamped and free-standing harvesters [7]. The most widely used clamped harvester is the Current Transformer (CT) [3, 8–10], which is based on a magnetic core in the near vicinity of the line. However, its bulky structure hinders the integration of more miniaturized wireless sensor systems. The coil dimensions tend to increase, affecting the whole system form factor due to the constraint of being correspondent to the range of the current flowing in the transmission line conductor [11]. Energy harvesting from the radial field produced by the high-power transmission line can be implemented coexisting with RF energy harvesting [10, 12] in the environment surrounding the conductors or substations. The harvesters of EM waves radiated by the transmission lines use rectifier circuits with different constraints than the design consideration of RF rectifiers [12]. An alternative to the bulky CT is the cantilever beam with a magnet attached to the tip [3, 13]. This technique uses magnets as an intermediate transducer that are displaced by the line EM field at the oscillating frequency of the current in the conductors. Then, the piezoelectric material of the beam is stressed with the displacements producing electrical energy from which can be harvested between 100 and 850 μW. In [14], the authors proposed a cantilever encapsulated in a vacuum to reduce the mechanical damping due to the beam’s interaction with the air. On the other hand, cantilever beams can lose the piezoelectric properties with the time of use [11]. SCs are commonly leveraged as an energy source in the grid environments [9] due to their higher instantaneous power densities; however, the power delivered is significantly affected by the hour of the day, the weather conditions (clouds, snow, temperature) or eventually partial shading effects. The direct consequence of the mentioned phenomenons is a reduction or total suppression in the irradiance level of the light impinging the exposed photovoltaic area. From the system design point of view, these fluctuations in the delivered energy can be sorted by optimizing power management and storage strategies or integrating SC modules with EM energy harvesters as a complementary energy source [9, 15].

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The following table presents a selection of energy harvesting solutions applied to SG sensor and monitoring problems. The selected works aim to summarize proposals with recently implemented and experimentally validated energy harvesting generators in the SG ecosystem. Since data transmission has an important role in total power consumption, this selection also includes articles that collect data and send it to a different platform for processing and decision-making. The transmission can be done wireless or using high-power conductor lines. References

Energy source

Application

Communication technology

Location

Sun et al. [16]

Solar

PressureSensor

RFID

Stand alone

Luo et al. [10]

EM & RF

FaultDiagnosis

RFID

ConductorAttached

Pehlivanoglu et al. [17]

EM

–

BPSKModulationa

Stand Alone

Wang et al. [8] Faheen et al. [12]

Piezo

CurrentSensor FaultDetection

–

ConductorAttached Stand Alone

Zhuo et al. [9]

EM & Solar

Wind Speed Sensor

Connected PC

Parejo et al. [18] Yuan et al. [11]

EM

FaultDetection Temperature Sensor

PLC Modem (SFSK) Alarm-System

a Implemented

EM & RF

Electrostatic

TinyOS Mica2

to-

ConductorAttached 52 mA ConductorAttached

Harvestedconsumed parameters 15 mA-for 730 ms every 30 min 0.54–0.68 V at rectifier’s input 40 mJ harvested in 15 min 8.68 mW in 30A-line Wifi 33 mW. EM 285 mW at 1K load Solar 4.8 mW. EM 4.6 mW ConductorAttached 0.78 mW maximum for 30 M load

as a numerical model

3.2 Mechanical Energy Sources Mechanical energy is present in nature in a diverse group of physics phenomenons such as kinetics, in the form of translations or rotations, as mechanical potential in the movement of a force field and elastic deformation, as periodic changes between potential, and as mechanical impulses. In the grid environment, low-frequency vibration can be observed in the power lines produced by the interaction with the wind. Mechanical oscillations can also be generated by magnets periodically dislocated by the EM field radiated by the high-power conductors. Other vibration sources can

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Fig. 2 Model diagram of a mechanical oscillating system. The seismic mass can be excited by the movements of the frame or the seismic mass itself

Mechanical Spring Sismic Mass

bm

bel Frame

be found in the substation locations, such as those produced by working engines, pumps, or refrigeration systems. At the beginning of Sect. 3, the main processes to convert mechanical perturbations into electrical energy was mentioned. Within this group, the piezoelectric and electrostatic effects have the particularity that can be used as intermediate transducers to convert the mechanical energy present in magnets inserted in variable magnetic fields into electric energy profitable for energizing low-power devices.

3.2.1

Harmonic Mechanical Oscillator

These schemes can be abstracted to a mechanical resonant model based on the dominant frequency of their oscillations. Figure 2 shows the model diagram, composed of a mechanical spring with a spring constant denoted by k, a seismic mass attached to the spring, and the losses, which are represented as clamping coefficients for electrical (bel ) and mechanical energy (bm ). All components are inserted into a frame housing the system. The system generates energy excited by vibrations happening in the frame, amplified and transmitted to the seismic mass by the spring, and then converted to usable electric power. Since the maximal amplitude of the seismic mass movement occurs at the resonance frequency, the maximal energy extraction is conditioned by this frequency; hence, the system should be tuned to optimize the harvesting process.

At the same time, there is a trade-off in the quantity of energy extracted from the system. The more the harvested power, the more kinetic energy is subtracted from the moving seismic mass, which can eventually stop the movement, making it impossible to obtain further energy. Hence, the system should be optimized to achieve an electrical damping (ζel ) equal in value to the mechanical damping (ζm ).

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Quality Factor The quality factor (Q) in the harmonic mechanical oscillator model indicates the quantity of energy the seismic mass can store. Q defines the ratio between the maximal amplitude of the seismic mass movement and the excitation amplitude at resonance. The amplitude of the seismic mass movement can be calculated as follows: |Z | = 

ω2 |Y | (ωo2 − ω2 )2 + [2(ζel + ζm )ωo ω]2

(1)

where ω and ωo are the angular and the angular resonance frequencies, respectively. |Y | is the amplitude of the excitation, and ζel + ζm = ζT represents the total damping factor as the sum of the electrical and the mechanical damping factors. At resonance, ω = ωo , then: Q=

|Z | 1 = |Y | 2(ζel + ζm )

(2)

From Eq. 2 can be seen that a higher Q can be achieved by reducing the total damping factor in the system. Consequently, the average power dissipated by the dampers is [19]: Pav

 3 mζT |Y |2 ωωo ω3 =  2 2   ω 2 1 − ωωo + 2ζT ωo

(3)

where m is the seismic mass. The limits in the power extraction of the model are conditioned by the resonance stage where Pav turns: Pmax =

m|Y |2 ωo3 4ζT

(4)

demonstrating that the extracted power is directly proportional to the resonance frequency.

3.2.2

Piezoelectric Generators

The generation of electrical energy via the piezoelectric effect is explained as the charge displacement due to the mechanical deformation of the material in an asymmetric piezoelectric lattice. Straining, stressing, or distorting the material produces the charges’ vertical or horizontal dislocation. The longer the electrical displacement, the higher the energy available to be harvested. Material parameters such as the dielectric constant and the piezoelectric strain coefficient are directly related to the freedom for charge dislocation and are proportional to it. Another critical parameter is the piezoelectric coupling coefficient, quantifying the system’s ability to produce

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load metal electrode

movement under external force

fixed edge

X

Z

Z=0

magnet piezo disk stiffening layer

Fig. 3 Diagram of a cantilever beam with a magnet attached to one edge. The stiffening layer guarantees the generation of a unidirectional stress in the piezo disk

electrical energy as the squared ratio of the electrical energy stored to the mechanical energy applied [20]. Piezoelectric energy harvesting systems applied into a grid environment often use magnets fixed to a cantilever to generate mechanical oscillations out of the EM radiation coming from the conductor lines. Figure 3 present a diagram of the hardware. In this case, the piezoelectric generator (cantilever) is used as the spring of Fig. 2 attached to the seismic mass (magnet). The electrical power is collected by metal electrodes stacked on the piezo and stiffening materials and transferred to the load. The position of the beam/magnet structure with respect to the orientation of the magnetic field defines the direction of the mechanical stress applied to the piezoelectric beam during the excitation. Electrical Equivalent Circuit Model The electromechanical problem represented in Fig. 3 can be modeled in the electrical domain, and the optimal load can be obtained by impedance matching approach in an equivalent circuit model [20–23]. A piezoelectric energy harvester operating at its resonance frequency can be defined as a linear single-degree-of-freedom model using the following: f ext (t) = m

∂ 2w ∂w (t) + K w(t) + θ ν p (t) (t) + C 2 ∂t ∂t

(5)

q(t) = θ w(t) + C p ν p (t)

(6)

where f ext is the externally applied force, w is the beam temporal displacement with respect to the fixed edge, C is the damping, K is the stiffness, θ is the electromechan-

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13

Fig. 4 Electromechanical problem of a piezoelectric beam assuming linear single-degree-of-freedom being modeled as an equivalent electrical circuit model

ical coupling, q is the charge, C p is the effective capacitance between the layers and ν p is the voltage as a function of time. f ext in terms of the acceleration of clamped boundary condition a(t) can be expressed as: f ext = a(t)D

(7)

where D is the effective input mass [20]. Hence, by substituting Eq. 7 in Eq. 5, dividing all the expression by θ and treating the derivatives, Eq. 5 can be rewritten as: νeq (t) =

m˙ C θ2 i eq (t) + ν p (t) (t) + i (t) + i eq eq θ2 θ2 K

(8)

where νeq (t) = a(t)D and i eq = θ w(t). Equation 8 can be seen as Kirchhoff’s Law of the voltage in the mesh I of the circuit of Fig. 4. Here: Ls =

m C θ ; Rs = 2 ; C = θ2 θ K

(9)

Finally, Eq. 6 characterizes the equivalent current expressed as follows: i eq (t) =

ν˙ p (t) + i p (t) Cp

(10)

The electrical circuit model of the piezoelectric beam allows the application of the impedance matching principle for maximal power transfer from the source to the load. Previously, the system has been parameterized, and it can be further optimized by complex-conjugated matching between the source and the load. In that way, since resistive energy harvesting systems have zero reactance at the resonance frequency, the optimal resistance can be found as follows: Ropt =

2k 2 ζT2 1 wC p 4ζT2 + k 4

(11)

where the dimensionless variables damping ratio and effective electromechanical coupling coefficient are defined as follows:

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C θ2 ζT = √ ; k2 = CpK 2 Km 3.2.3

(12)

Electromagnetic and Electrostatic Harvester

Mechanical harvesters using EM or electrostatic principles are based on employing mechanical movements to variate the electric or magnetic field distribution within parallel metallic plates or conductors in the form of a coil. Faraday’s law of induction, presented in Eq. 13, states that the movement of a loop conductor, changing the magnetic field traversing its enclosing area, can produce an electric current on it. The current can be created either by displacement of the coil or the source of the magnetic field. On the other hand, varying the distance between metallic plates changes the redistribution of electric charges in a variable capacitor. Electromagnetic generators An induced voltage can be generated across two coil terminals by varying the magnetic flux within the loops of the conductor. Equation 13 shows the relationship, where the induced voltage U is directly proportional to the number of turns in the coil N . As mentioned above, the electrical energy can be harvested with a moving magnet or coil; however, the former has the advantage of ensuring fixed electrical connections, which makes it more robust against aging. In addition, the total weight of the moving mass is smaller. U = −N

dφ dt

(13)

The efficiency of the harvesting process can be improved by integrating the same system: a permanent coil and a moving magnet attached to a cantilever beam used as the spring. Thus, mechanical energy will change the magnetic flux through the coil loops while stressing the piezoelectric material of the cantilever beam. Consequently, the total harvested power will increase. Applying the harmonic mechanical model described in Sect. 3.2.1, maximal power can be extracted by making: ζm = ζel =

k2 1 · 2mwo Ro + R L

(14)

Here, Ro and R L are the coil and load resistance, respectively. k is the electromagnetic coupling coefficient defined as the ratio between the induced voltage U and the 2 velocity of the seismic mass z  . Solving U and substituting RoU+R L in Eq. 14, the maximum delivered power can be found as: Pmax = 2 · ζel · m · wo · z  (t)2

(15)

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Electrostatic generators The energy harvesting process with electrostatic generators can be explained through a capacitor with variable capacitance Cvar depending on the instantaneous distance between its plates. The possibility of mechanically changing the capacitance by moving the plates allows it to act as a transducer supplying electrical energy to a second capacitor that stores the scavenged power, which will then be worked on the load. When Cvar is altered, the charge distribution is modified following two approaches. The first one is voltage constrained, where the plates are approached a specific distance achieving maximum capacitance (Cmax ), then the capacitor is charged up to the maximum voltage (Vmax ). The external force is harnessed to separate the plates, decreasing the capacitance. Since the voltage is constrained to Vmax , the charges will flow, generating the electricity. Assuming the minimum capacitance (Cmin ) happens at the maximum possible separation, then the electrical energy extracted will follow the expression: EV O L =

1 2 (Cmax − Cmin )Vmax 2

(16)

The second method charges a variable capacitor up to an initial voltage (Vo ), separating the plates to a maximum distance, decreasing the capacitance and increasing voltage. This harvesting variant is known as the charge-constrained method. At the maximum plate displacement, the capacitor is open-circuited and should be shortcircuited to transfer the charges to a second capacitor for storage. In this case, the obtained energy is: EC H =

1 (Cmax − Cmin )Vo Vmax 2

(17)

It can be intuitively noted that the voltage-constrained approach eventually delivers more energy due to Vmax being higher than Vo . Nonetheless, initially charging to a maximum voltage requires using an external voltage source that supplies this initial voltage.

3.3 Low-Power Solar Cells Fundamentals The energy emanating from the sun that reaches outside the Earth’s atmosphere is estimated at approximately 1400 W/m2 , characterized by the magnitude Irradiance. The atmosphere then absorbs part of this energy, and the resultant power density over the Earth’s surface is assumed as 1000 W/m2 . Using this inexhaustible source of power, generated without environmental pollution, is possible due to the discovery of the photoelectric effect. This phenomenon, observed by Heinrich Hertz in 1887 [24], is a primary optical principle in which photons hit a photovoltaic surface (typically on

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photosensitive semiconductors) with enough energy to make the electrons jump from the valence band to the conduction band. The charge movement produces electric power that can be harvested at any location on Earth but is subjected to constraints associated with the daytime and the sun’s position, the latitude of the harvesting site, and weather conditions. As the mass manufacturing cost of electronic components and SC technologies is going down, using solar energy as a power source for WSN and IoT devices, among others, has become an affordable and good cost-benefit solution. The SC technology represents a suitable choice for capturing renewable and environmentally friendly energy to supply low-energy devices, which often operate in conditions of low solar lighting or artificial lights with variable wavelength spectrum. The optimal performance of a SC is tested following Standard Test Conditions (STC), grouping parameters the Air Mass coefficient equal to 1.5, a power density of 1000 W/m2 and 25◦ C surrounding temperature (approximately 298 K). However, operation under STC can be ensured only in a controlled lab environment or in short time frames in outdoor measurement sites. A significant concern related to the energy coming from natural or artificial sources is that the supplied power is not always constant, so appropriate precautions must be taken to guarantee the correct operation of the device to be powered. Also, the illumination levels in indoor environments are inferior to outdoors, with a reduced and narrow band wavelength diversity when compared with the light from the sun. Most of the light sources currently used in interior lighting are fluorescent lamps, incandescent lamps, and white Light-EmitterDiode (LED) lamps. They emit light with wavelengths in the range of 350 nm to 750 nm, which is broader than the visible spectrum of the human eye. Hence, different SC materials have different absorption coefficients associated with the wavelengths for which the SC is sensitive. In addition, available power densities are defined by the distance from the light source or by the possibility of obstacles blocking the light. SCs can be built in different shapes and employ different types of semiconductors. Silicon is the most used and can be found in monocrystalline, polycrystalline, or amorphous varieties. One parameter that strongly influences the amount of power generated by a SC is the type of semiconductor material and which part of the wavelength spectrum is more photosensitive. As sunlight contains various wavelengths, a technique to optimize the amount of energy extracted is to stack different photosensitive materials working on more bands of the spectrum at the same time. Most commercial SCs are based on mono, and polycrystalline silicon wafers fabricated using the same silicon as raw material. However, the production process for polycrystalline cells is cheaper than the monocrystalline process. Consequently, the final product presents higher impurities and lower efficiency. A recently developed and exploited technology with several solutions already established in the market is the named thin-film SC [25]. This technology can be separated into three main groups: organic, Dye-sensitized, or perovskites, depending on the material’s nature. Typical SC structures, as seen in Fig. 5, consist of a silicon wafer sandwiched between two terminals, with an anode as the positive contact and a cathode as the negative. In most cases, the cathode is a solid sheet of conductive material, and the anode is a grid made up of fingers with an optimized number, width, and separation,

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17

Fig. 5 Typical structure of a SC. The higher the number of buses and fingers, the smaller the parasitic resistances opposing the circulation of the generated current. However, the negative terminal is placed over the photosensitive surface, reflecting some photons before the semiconductor material can absorb them

aiming to achieve a good agreement between the area of the photovoltaic surface that is blocked by the fingers, and the parasitic resistances of the SC.

3.3.1

Solar Cell Characterization

In practice, a SC can be modeled as an ideal current source, connected to a shunt diode representing the recombination current in the quasi-neutral region of the PN junction. A more accurate model can include a second diode in parallel, representing the recombination in the depletion region; however, the One-diode model is preferred due to its simplicity and satisfactory approximation to the reality [27]. In Fig. 6, I ph , Id , and I pv , respectively, represent the SC photo-generated current source, the diode current and the output current of the cell. Rsh and Rs are the equivalent shunt and series resistance, being Vo the output voltage of the SC. The derivation of the equivalent circuit model in a real SC is subjected to the experimental obtaining of its current and voltage characteristic curves.

Fig. 6 One-diode equivalent circuit model of a SC published in [26]. The current source drives the circuit, whose value depends mainly on the irradiance level and temperature

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Using the One-diode model, Eq. 18 characterizes the current flowing through to the diode, and Eq. 19 is the general expression for the current produced by a SC. Note that the SC generates current until Vo is higher than VT . At this point, the diode is directly biased, and the Id increases, making I pv drop.  Vo  (18) Id = Isat e AVT Ns − 1  Vo +I pv Rs  V +I R o pv s I pv = I ph − Isat e AVT − 1 − Rsh 



(19)

Id

In the last equations, Isat is the dark saturation current, A is the semiconductor ideality factor, VT is the voltage threshold of the semiconductor, and Ns is the number of SCs that compose the photovoltaic module. By knowing the semiconductor characteristics and having the SC’s experimental Voltage and Current curves, each of the components in the equivalent circuit can be derived mathematically.

Current and Voltage (I-V) Curves The current-voltage (I-V) characteristic of a generic SC is plotted in Fig. 7, and it illustrates the principal figures-of-merits for SC. These parameters are the Maximum Power Point (MPP), with its associated voltage and current (Vmp and Imp ), the short-circuit current (Isc ), and the open-circuit voltage (Voc ). The energy conversion efficiency η SC evaluates the energy sources performance, and it is defined by the ratio of the maximum converted power to the radiation power impinging the photovoltaic surface: η SC =

Imp Vmp G i A pvs

(20)

where: G i is the irradiance at operation condition, and A pvs is the area of a SC exposed to the light.

In laboratory environments, the G i values can be generated with a calibrated Solar Simulator to obtain the desired operation points according to the irradiance levels. A generic block diagram and a picture of a characterization setup can be seen in Fig. 8. Solar simulators using beam canons have the disadvantage of guaranteeing homogeneous light with the desired irradiance level at a restricted area which must cover the entire SC. This type of Solar Simulator (e.g., the Newport 96000) is more common in researching new materials to design new SC technologies. Once the SC is illuminated, its I-V curves can be extracted with a Precision Source/Measure Unit.

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Fig. 7 Current and power curves against voltage for a generic SC. The dotted curve represents the variation of the current, and the solid one represents the power behavior, published on [28]

Laboratory characterization of the SC shall be done under controlled temperature, typically at 298 K. Another essential parameter is the fill factor (FF), defined in Eq. 21 as the ratio of the maximum power produced by a SC and the ideal maximum power found by multiplying Isc and Voc : FF =

Imp Vmp Isc Voc

(21)

Satisfactory FF values are estimated between 60 and 85% [29]. A higher FF results from a Rs value closer to zero and a Rsh with an elevated resistance, representing a quantitative tool to assess the SC quality.

3.3.2

Maximum Power Point Tracking

The analysis of SC performance is conditioned by estimating the possible delivered power according to external influences. Climate changes such as the presence of clouds, variation in irradiance and temperature due to the hour of the day, or partial shading effect are examples of phenomena that provocate variations of the power generated by the SC. The maximum power point will oscillate for each situation, changing the power conversion efficiency. A way of guaranteeing in every moment the extraction of the maximal possible energy is the implementation of MPP Tracking (MPPT) techniques. MPPT methods continuously sense the voltage and current at the SC output and manipulate its load to find the optimum extracted power. In low-power applications, the most commonly used algorithms are the Fractional Open-Circuit Voltage (F.OCV) and the also known as Hill Climbing techniques: Perturb-andObserve (PO) and Incremental Conductance (IC) [30]. F.OCV is based on knowing the SC Voc and adjusting the load to obtain an output power at 70 to 80% of the power for Voc . This 0.7–0.8 ratio is fixed and can be straightforwardly set by hardware or software. However, although this method has low complexity for its hardware or software implementation, the tracking ability is reduced due to the heavy dependence on the SC’s Voc . Hill Climbing techniques

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Fig. 8 a Block diagram of a generic characterization setup to extract I-V curves of SC. Solar Simulators can also be found in the form of irradiance chambers where a wider area can be irradiated at a specific and homogeneous irradiance level, published on [28]. b Picture of a laboratory setup for SC evaluation using a beam cannon published on [26]

have a better tracking ability since they find the maximum power by comparing to a previous stage after an increment (PO case) or by finding where the power curve derivative, as a function of the voltage at the output, is equal to zero (IC case).

3.3.3

Temperature Influence on the SC MPP

One external condition affecting the SC output power is the temperature variation in the vicinity of the photovoltaic material. As the SC temperature can change depending on the environment where it is located, it can result in a deviation from the typical 298 K specified in the STC. SC modules used for energy harvesting are intended to operate under low-power conditions. A shit of the SC MPP is a direct consequence

Energy Harvesting Towards Power Autonomous Sensors in Smart Grids

21

of the mentioned fluctuations, which might displace the available DC voltage level, compromising the continuous operation of the application. Looking at the SC equivalent circuit (Fig. 6), the variation of the output current as a function of the temperature is expressed in Eq. 19 by I ph and Id . The thermal voltage is Vt = kT /q, where T is the ambient temperature in Kelvin, k is the constant of Boltzmann, and q is the electron charge. In the circuit of Fig. 6, I ph is represented as an ideal current source whose value remains constant if there is no variation of the irradiance or the temperature. Nevertheless, its behavior is described by:  Isc + ki (T − Tr e f ) G i (22) I ph = Gr e f where: ki is the short-circuit temperature coefficient. The Tr e f and G r e f are the reference irradiance, and temperature for the conditions analyzed, typically for STC. Equation 22 models the ideal current I ph and the temperature can be swept while recording the values of Imp , Vmp , and Pmp . The temperature reduces the bandgap energy (E g ) of the material increasing Isc [31]: E g (T ) = E g (0) −

αT 2 T + βt

(23)

where: E g (0) is the 0 K bandgap energy. α and βt are semiconductor material constants. On the other hand, Voc is decreasing at a higher rate than the current increment. Hence, the lower the E g , the higher the intrinsic carrier concentration, increasing Isat and affecting Voc . The relation between Voc and the Isat can be found as: 

Isc kT (24) ln Voc = q Isat and Isat = q As

D f n i2 Lc ND

(25)

where: As is the area of the semiconductor material, D f is diffusivity of the minority carriers, L c is the minority carrier diffusion length, N D is the doping of the semiconductor and n i is the intrinsic carrier concentration. In Eq. 25, D f , L, and n i depend on temperature variations. However, the biggest effect is caused by the influence of n i since it is a power of 2 and directly depends on T . Then, the square of n i can be calculated as:

n i2 = 4

2π kT h 2P

3



m ∗e m ∗h

 23



e

−

E g (0) kT



(26)

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where: h P is the Plank’s constant, and m e and m h are the electrons and holes effective masses, respectively. By isolating the values essentially independent of temperature as:

B=4

2π k h2

3

 ∗ ∗  23 me mh

(27)

Equation 26 can be rewritten as: 

n i2 = BT 3 e

−

E g (0) kT



(28)

By substituting n i2 from Eq. 28 in Eq. 25 and neglecting the temperature dependence of D f and L c , the saturation current can be expressed as: Isat ≈ BT 3 e

  E g (0) − kT

(29)

If Eq. 29 is substituted in Eq. 24, the impact of Isat over Voc as a function of temperature variations can be derived as:

 q Vg (0) KT ln Isc − ln B − 3lnT + (30) Voc = q kT where: E g (0) = q Vg (0). Thus, Voc − Vg (0) 3k d Voc = − dT T q

(31)

In this way, it demonstrated that Voc presents a nonlinear behavior associated with the variation of the intrinsic carrier concentration and is inversely proportional to the temperature increment.

3.4 Radio Frequency Energy Harvesting At the end of the 19th century, in the United States, Nikola Tesla was responsible for presenting and demonstrating the concept of WPT [32]. In order to make WPT a commercial technology, Tesla idealized a wireless power transmission station capable of sending signals across the Atlantic Ocean called the Wardenclyffe Tower. The project had a transmission power of 300 kW at 150 kHz; however, it was not completed due to the high cost of building the tower. The next big breakthrough in the WPT field came only in 1964 when William C. Brown associated antennas and RF rectifiers to create a rectifying antenna, the basic structure of the far-field WPT reception system. For this, he combined half-wave dipole antennas with a series of rectifiers to power the electric motor of a prototype

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23

Fig. 9 Block diagram of a rectenna. The RF signal captured by the antenna is then transferred according to the impedance matching network to the rectifier, where it is converted to DC. The DC filter eliminates the by-products of this rectification process before the power is delivered to the load

helicopter, performing a flight with a maximum height of 18 m for more than ten consecutive hours [33]. Since then, advances in the WPT area have been gradual and exploratory, presenting variations in techniques, architectures, and strategies. However, without offering a technological breakthrough that surpasses all other current methods. In other words, most RF energy harvesting research nowadays aims to optimize an already well-established structure, the rectenna. Rectenna A Rectenna is a transducer composed of an antenna for picking up electromagnetic energy and a rectifier that converts the energy in the captured waves into a DC level. The antenna can be optimized to work within near-field distances using the induction coupling principle or to operate in the far-field region, as a power source for low-consumption devices.

The basic architecture of an RF energy harvesting receiver, the rectenna, is represented in the block diagram shown in Fig. 9. In general, this circuit’s function is to convert RF power density (S AV ) present in the environment into DC electrical power. The antenna is the first stage of the reception system, and it converts the electromagnetic power density present in the environment to electrical power confined to the device, still in the RF domain. In the next stage, the impedance matching network is added to magnify the power transfer between adjacent nodes. The matching circuit introduces real and imaginary impedance to achieve the complex conjugate impedance of the previous stage at its input and the complex conjugate impedance of the next stage at its output. This impedance can be ideally adjusted through capacitive and inductive lumped or distributed elements, such as surface mount components and open or short stubs in the RF transmission lines (do not mistake the transmission line in an electrical power system context). The rectifier itself is the third stage, and promotes the RF to DC energy conversion. The conversion is done by associating one or more diodes, which must perform well

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Fig. 10 Schematics of the main RF rectifier topologies for low-power applications

at high frequencies and have a low threshold voltage, like the usually used Schottky diodes. The rectifier topology (how the diodes are associated) should vary depending on the application and the power levels involved. The rectenna’s fourth stage is the DC filter (or low-pass filter). Its function is to eliminate high-frequency oscillations and harmonics after rectification and deliver higher-quality power to the load, the last stage. The DC filter is usually composed of a capacitor connected to the ground and a series inductor.

3.4.1

Rectifiers Topologies

Among the main RF rectifier topologies, there are four widely used in low-power applications: series, shunt, doubler, and symmetric. These topologies are shown in Fig. 10, where the voltage source (VI N ) plus the equivalent series impedance (Z S ), at the rectifier input, represents the antenna modeling after its equivalent Thévenin circuit. For each RF rectifier topology previously mentioned, the following elements are essential for an optimized point of operation: impedance matching network (Z line ); diode (D); capacitive filter at the output (Co ); and resistive load (Rl ). The output voltage across Rl is defined by Vo . The series topology (Fig. 10a) works ideally as a half-wave rectifier, with D conducting all along the positive cycle of VI N . Guaranteeing this performance needs the inclusion of a DC short to the anode terminal side of the diode. Its main scope is to suppress charge buildup, which arises from the diode depletion region specifically due to RF sources with Z S equivalent to an open circuit for DC. On the contrary, the

Energy Harvesting Towards Power Autonomous Sensors in Smart Grids

25

Fig. 11 Efficiency of rectifier topologies, with their respective optimum load. At lower power levels, the simple (single-diode) topologies perform better. In contrast, the others performed better at higher power levels [34]

shunt topology, (Fig. 10b), must contain a DC block (capacitor) connected between the diode and Z line to prevent reverse current flow from the circuit output to the circuit input. For the symmetrical topology (Fig. 10c), it can be interpreted as the junction of two series rectifiers, composed by Z line1 , D1 , and Co1 for the positive cycle of VI N , and by Z line2 , D2 , and Co2 during the negative one. Assuming the same understanding for the case of the doubler topology (Fig. 10d), it can be presented as the coupling of a series and a shunt rectifier. Furthermore, it is crucial to highlight that a short circuit to DC is needed at the input side of the circuit exclusively for the doubler topology. In addition, the capacitor C1 operates as an electrical charge retainer over the negative cycle of the input signal conducting in series with D2 , and pumping electric charge over the positive cycle in series with D1 . Additionally, to the two diode topologies, D1 and D2 conduct in opposite semicycles of the input signal, thus increasing the rectifying period of the circuit. Another advantage of these topologies is that the maximum reverse voltage across each diode is divided at least in half. However, for lower power levels, the simpler topologies (two firsts) tend to have better performance since these topologies have fewer diodes. Figure 11 shows the performance difference of each topology by varying the input power.

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Fig. 12 Equivalent small signal diode model with package parasitics

3.4.2

Diode Model in an RF Energy Harvesting Context

Given the critical role played by the diode in the RF rectifier efficiency, it is crucial to promote a mathematical model to predict the performance of this device in an RF energy harvesting context. For RF circuits, parasitic effects related to the component encapsulation have an expressive impact over losses [35, 36] and, in this case, on the reactive part of the rectifier input impedance (Z in ), evidenced by the diode’s parasitic capacitance. Therefore, due to the component’s nonlinearity and the encapsulation’s parasitic effects, it becomes crucial to derivate a circuit model that represents the behavior of the different component impedances. It must be a model that recreates the response related to Pin and the operating frequency of the input signal, using small signal analysis [37]. As can be seen in Fig. 12, this can be done by the addition of a parallel parasitic capacitance (C p ) and a series parasitic inductance (L p ) to the conventional equivalent model of the diode, as described in [37–39]. In Fig. 12, Rs is the internal series resistance, invariant with the signal input frequency and power. The resistance R j , and the junction capacitance C j , are directly related to the voltage across the diode (Vd ), as shown by (32) and (33). Rj =

8.33 × 10−5 nT Is + Id

C j0 Cj =  m 1 − VVdj  Vd  Id = Is e nVt − 1

(32)

(33)

(34)

Where V j is the junction voltage, C j0 is the zero voltage junction capacitance, m is the grading coefficient, Is the saturation current, n the ideality factor, and T is the temperature coefficient in Kelvin. In these equations, only R j is not directly related to Vd .

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Fig. 13 Equivalent circuit for an RF operating diode considering the reverse diode conduction plus the enclosure [41]

Equation (35) is generated by substituiting (34) in (32) and applying Taylor expansion to the exponential, followed by truncating at the second-order term. This equation describes the relationship between R j and Vd , highlighting that the equation is limited to the small signals modeling.   1 V 2 nVt R j = 8.33 × 10−5 nT Is−1 1 − Vd + d 2

(35)

Therefore, the diode impedance, Z d , as a function of the angular frequency, ω, is expressed by:  Z d (ω) =

3.4.3

1 (( jωC j +

1 −1 ) Rj

+ Rs )

+ jωC p

−1

+ jωL s

(36)

Influence of Diode Reverse Conduction on the Rectifier

As mentioned at the beginning of the section, Schottky diodes are commonly applied in RF energy harvesting rectifiers in virtue of their fast switching capability, low forward voltage drop and GHz operating frequency range [40]. However, these diodes also present a lower breakdown voltage (BV) in comparison to standard silicon ones, which significantly affects the performance of the rectifiers. To consider the influence of both, direct and reverse conductions, the diode can be modeled as shown in Fig. 13, where i Dr represents the diode reverse current. To

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determine the rectifier Z in , vo and the Power Conversion Efficiency (PCE), a circuit analysis can be performed, resulting in a set of four differential equations [41]: di G = ix dt dvo 1 i Co = = dt Co Co

 vo iG − Rl

(37)

(38)

dv D 1 = [vG − L P i x − v D − R S (i D − i Dr ) − vo ] dt RS C j

(39)



 di x dvG 1 dvo dv D i D − i Dr − i G + C j = + CP − dt L PCP dt dt dt

(40)

where i x has been introduced to abstain a second order differential term, i D and i Dr are obtained by:  vD  (41) i D = I S e ηVt − 1  (v D −VBV )  i Dr = I BV e− ηr Vt

(42)

where η and ηr are the ideality factors for direct and reverse currents, respectively, I S is the saturation current, VBV is the reverse breakdown voltage, I BV is the current at breakdown region, Vt = kT /q is the thermal voltage, q is the electron charge, k is the Boltzmann constant, and T is the absolute temperature. Considering a sinusoidal voltage source applied to vG , the system can be solved, where the mean value of vo can be considered as the DC output voltage after the rectification, through which the PCE can be calculated. Following [40], the FastFourier Transform (FFT) of the generator current (i G ) can be obtained numerically, and the ratio of the source voltage to i G first-order harmonic component results in Z in . The influence of the diode reverse conduction can be analyzed by considering and neglecting i Dr in (40). Figure 14 presents the PCE as a function of the input power for three different commercial Schottky barrier diodes, where the solid and dashed lines describe the PCE by omitting and considering, respectively, the diode reverse conduction. The curves represented by the symbols were obtained through the software Advanced Design System (ADS) from Keysight to assure the effectiveness of the model. A significant PCE drop can be observed when considering the reverse conduction for higher Pin , due to the low VBV of such devices. When Pin is increased, but the input power is not enough for reverse conduction, vo and, consequently, Pout also increase. However, for even higher Pin , when v Dr reaches its VBV , vo is smoothly

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Fig. 14 PCE related to Pin for different diodes demonstrating the influence of the breakdown voltage [41]

set to a constant value, such that Pout also tends to a constant. Consequently, further Pin increase results in a significant PCE drop.

3.4.4

Temperature Influence on the Rectification

Besides to the effect of the reverse conduction, the rectifier can also be severely affected by the temperature of operation. The main diode parameters affected by the temperature are I S , V j , and C j0 [42, 43]. At zero Kelvin, the electronic states in semiconductors are all occupied from the lowest energy level and continue upward until all electrons are considered [44], such that there are no electrons in the conduction band for an intrinsic semiconductor. When T increases, thermal motion leads to excitation of electrons from the valence band to the conduction band, increasing the intrinsic carrier density (n i ) [45]. For a Schottky diode, one of the main conduction mechanisms through the barrier is the thermionic emission, which occurs when a thermally active electron has enough energy to overcome the barrier [45]. This mechanism is strongly temperature dependent and is considered in the parameter I S , which increases with T. Moreover, the junction voltage, related to the barrier height, has been shown to reduce with the temperature increase, such that the junction capacitance increases with T. To analyze the temperature influence, the system of differential equations (37)– (40) has been solved for a commercial Schottky diode, and the results are shown in Fig. 15. The analysis has considered a load of 5k and a T range between 240 and 360 K. The curves demonstrate the significant influence of T on PCE. The maximum PCE is obtained at T = 300 K for any Pin in the studied range. However, PCE reduces from 65% at Pin = −10 dBm to lower than 3% when T increases to 360 K. For T lower than 300 K, a slight reduction in the PCE is also observed, e.g., 57 % at 240 K. In Fig. 16, the PCE is shown as a Pin function for the same device of Fig. 15, considering a load of 1k. The maximum PCE for Pin = −10 dBm is obtained at T = 280 K and values 54%. When reducing T to 240 K, PCE reduces to 10% whereas PCE = 37% for 360 K. By comparing both figures, it can be concluded that the PCE

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Fig. 15 PCE related to Pin for the diode SMS7630 with package SC-79 demonstrating the temperature influence for a load of 5k [41]

Fig. 16 PCE related to Pin for the diode SMS7630 with package SC-79 demonstrating the temperature influence for a load of 1k

is significantly dependent on both T and R L , which makes the optimization of such rectifiers a cumbersome task. In addition, the maximum PCE point, which is related to the reverse conduction, has also been affected by T and R L . From Figs. 15 and 16, it can be noted that the sudden reduction due to the diode reverse conduction can occur independently of the load or temperature. As mentioned before, for higher Pin , when v Dr reaches its VBV , vo is smoothly set to a constant value, such that Pout also goes to a constant value. This effect can be clearly seen in Fig. 17, where v0 is shown as a function of Pin for the temperatures varying from 280 to 360 K. Once the reverse conduction starts, v0 remains constant for any higher value of Pin , degrading PCE. Another important point for the RF energy harvesting circuit modeling is that the antenna’s impedance should match the rectifier one. Generally, a matching network is used between the rectifier and the antenna to maximize the power transfer. However, the rectifier Z in depends not only on the frequency, but also on the power level and temperature [37, 46, 47]. By applying the Fast-Fourier Transform (FFT) of the generator current (i G ) calculated through the system of differential equations (37)– (40), Z in is acquired by the ratio of the source voltage to the i G first-order harmonic component. To analyze T and Pin influence on Z in , the real (Rin ) and imaginary components (X in ) have been obtained and the results are shown in Figs. 18 and 19. As mentioned,

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Fig. 17 Output voltage related to Pin for the diode SMS7630 with package SC-79 demonstrating the temperature influence for a load of 5k

Fig. 18 Real component of Z in according to Pin for the diode SMS7630 with package SC-79 demonstrating the temperature influence for a load of 5k [41]

the temperature rise causes an increase in I S , which reduces the junction resistance. Besides that, the junction capacitance increases with T increase due to the reduction of V j . These influences result in a Rin increase with T, as shown in Fig. 18. For lower Pin , the Rin dependence on Pin is small, such that the small signal AC analysis, described by (36), can be applied to obtain the impedance. However, such an approximation could not be used for higher Pin , owing to the diode’s nonlinear behavior. For a Pin higher than –5 dBm, a sudden increase, similar for all temperatures, is observed as a result of the diode reverse conduction. In Fig. 19, the behavior of X in as a function of Pin for different temperatures is shown. In this case, the T increase results in a reduction of the absolute value of X in . For Pin higher than –5 dBm, a drop of |X in |, is observed due to the diode reverse conduction. Therefore, from Figs. 18 and 19, it can be concluded that defining the matching network required bounded by the antenna and the rectifier, is a challenging task since the input impedance varies significantly with T and Pin .

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Fig. 19 Imaginary component of Z in according to Pin for the diode SMS7630 with package SC-79 demonstrating the temperature influence for a load of 5k

4 Conclusions and Future Works Powering edge devices for monitoring and control in power grid environments is a critical topic for developing smart grids. This chapter presented different energy harvesting sources and techniques that have been proposed to support power to wireless sensors and actuators operating within power grid ecosystems. We summarized the main collection mechanisms from different ambient energy sources, such as mechanical, solar, and RF energy, which are shown to be promising techniques in an SG context. Nevertheless, for more information, contributions that are out of the mentioned selection have been referenced in the different surveys cited within this text. Energy harvesting in an SG environment is still in an early development stage, and this chapter aims to analyze one of the possible approaches: sources and energy harvesting techniques that can be used in SG edge devices. Among other approaches and challenges that reinforce this adherence between the themes, the following stand out: the structure and efficiency of the PMU, energy-saving strategies, communication scheduling, and operating strategies based on energy restriction, among others. Despite this, the unrestricted use of energy harvesting techniques in SG devices seems to be a matter of time. Acknowledgements This research was supported by Coordenaáo de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under grant 001, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) under grants 2019/25866-7, 2022/10876-0, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) under grant 404068/2020-0, Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) under grant APQ-03609-17 and Instituto Nacional de Energia Elétrica (INERGE).

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Off-Grid Green Hydrogen Production Systems Alejandro Ibáñez-Rioja, Georgios Sakas, Lauri Järvinen, and Pietari Puranen

Abstract This chapter introduces the role of hydrogen in the current energy system transition: from fossil-based to renewable and low-carbon emission sources. Although solar and wind energy are abundant renewable sources, the intermittence of electricity generation remains a challenge for security of supply and causes instabilities in the electricity grid. The integration of green hydrogen produced by water electrolysis into a smart energy system –or a smart grid–, is considered a promising solution to overcome the handicaps of the renewable electricity production and certain hard-to-decarbonize industrial sectors. The principle of water electrolysis along with the different electrolyzer technologies is also presented in the first section. In the second section, a numerical model of an industrial alkaline water electrolyzer plant is described. The different unit operators that comprise the system to produce purified hydrogen are individually introduced. The chapter concludes by showing the capabilities of an off-grid water electrolyzer system, which consists of a battery energy system and solar PV and wind power installations. Simulation of the plant demonstrates, as a proof of concept, the feasibility of the system for future integration into a smart energy system.

A. Ibáñez-Rioja (B) · G. Sakas · L. Järvinen · P. Puranen Lappeenranta–Lahti University of Technology LUT, P.O. Box 20, FI-53851 Lappeenranta, Finland e-mail: [email protected] G. Sakas e-mail: [email protected] L. Järvinen e-mail: [email protected] P. Puranen e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_2

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Nomenclature Acronyms AC AWE BESS DC FLHs ODE PEM PEMWE PID SEC

Alternating current Alkaline water electrolyzer Battery energy storage system Direct current Full load hours Ordinary differential equation Proton exchange membrane PEM water electrolyzer Proportional integral derivative Specific energy consumption

Variables α β  η ρ σ A C D F h I i k L M m˙ N n˙ P Q˙ R r s T

U–I curve model parameter U–I curve model parameter Emissivity Efficiency Density Stefan-Boltzmann constant Cross-sectional area Thermal capacitance Diameter Faraday constant Average heat transfer coefficient Current Current density Thermal conductivity Length Molarity Mass flow rate Total number Molar flow rate Pressure Power loss Resistance Reaction rate Tafel slope model parameter Temperature

Off-Grid Green Hydrogen Production Systems

U V z Mm Nu

Voltage Volume Number of moles of electrons transferred in the reaction Molar mass Nusselt number

Subscripts act amb an c cat cd cn cnv con ele F h imp i j liq loss m ohm pd rad rev rev,0 s sep shunt tn v w

Activation ambient Anode Cell Cathode cold consumption convection Concentration Electrolyte Faraday hot impurities Inlet Outlet liquid loss make-up feed Ohmic production radiation Reversible Standard equilibrium stack separation vessel shunt current thermoneutral Vapor Water

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1 Green Hydrogen 1.1 Hydrogen. What is it for? 1.1.1

Motivation: Issues with the Current Energy System

The first decades of the twenty-first century have shown an accelerated growth in the global economy and increasing industrialization worldwide. So far, the economic growth has been tightly coupled with the increasing energy consumption and exploitation of non-renewable natural resources. As the world’s population is still increasing, and ever more people have access to the western consumer lifestyle, the final energy and raw material consumption are expected to continue growing in the near future [1, 2]. Increasing consumption of non-renewable resources, both directly as a raw material and through burning for energy generation, causes a variety of environmental issues. Climate change caused by increasing greenhouse gas emissions in the Earth’s atmosphere is the best-known of these environmental problems [3]. The most prominent of the greenhouse gases is fossil carbon dioxide (CO2 ), whose main sources relate to the burning of fossil fuels for converting their chemical energy into electrical energy, heat or mechanical energy for final use. Furthermore, depleting non-renewable resources in easily accessible areas leads to their extraction from higher-risk areas, which increases both the cost of extraction and the probability of biodiversity degradation and environmental disasters. In addition to being the main cause of anthropogenic climate change, extreme reliance on fossil energy sources makes the global economy highly vulnerable to disruptions in the constant flow of fuel or its price. Political conflicts and wars between nations can influence the availability, and thus the price of fossil fuel products, potentially driving economies into recession [4]. Local or global energy crises of this kind have occurred several times in the past decades. Nations without their own fossil fuel reservoirs are the most vulnerable to such shocks in the global fuel trade.

1.1.2

Energy System Transition from Fossils to Renewables

One promising solution for both cutting the carbon dioxide emissions and ensuring the security of energy supply is wider electrification of the whole energy system and an increase in the exploitation of renewable energy sources, mainly onshore wind and solar energy [2]. The costs of both these energy generation techniques have plummeted in the last couple of decades, making them the cheapest sources of electricity as of 2022 [5, 6]. Because of their low price, the number of new solar and wind power installations has been growing exponentially, accounting for the majority of new electricity generation capacity installed between 2015 and 2021 [7]. Although, wind and solar energy are abundantly available nearly everywhere around the globe, their wider exploitation in the context of the current energy system

Off-Grid Green Hydrogen Production Systems

41

has two major issues. Firstly, the availability of wind and solar energy is highly intermittent both in the short and long term. The fluctuating nature of the energy sources gives rise to a considerable need for energy storage capacity to completely phase out fossil energy sources [5]. Secondly, sectors like mobility, industry, and heating, which have traditionally been reliant on directly burning fossil fuels, have to be as far electrified as possible to utilize the renewable electricity generation. Some of them can be at least partially directly electrified, like road traffic and domestic heating by using battery electric vehicles (BEV) and air-sourced or ground-sourced heat pumps, respectively. Unfortunately, not all sectors can be directly electrified, including aviation, marine transportation, and many high-temperature industrial processes. These sectors have to be, thus, indirectly electrified with the introduction of electrofuels (aka. e-fuels), such as hydrogen (H2 ), methane (CH4 ), methanol (CH3 OH or MeOH), or ammonia (NH3 ) that have to be produced without using fossil-based raw materials [8]. Hydrogen as an energy carrier has been proposed to be one possible solution for both of the above-mentioned issues. At the simplest, hydrogen can be produced by electrochemical water electrolysis requiring only water and electricity as the precursors. Conversion back to electricity is possible with the reverse electrochemical reaction in a fuel cell, but also combustion in a gas turbine is an alternative conversion technique. Oxidizing hydrogen in either a fuel cell or through combustion produces only water as the reaction product, which makes electrolytically produced hydrogen a truly clean fuel, as long as the input power used to produce the hydrogen has come from a clean source. The usage as an energy storage is not the sole use case for electrochemically produced hydrogen. Many useful fuels and commodity chemicals that have traditionally been based on fossil raw materials can also be produced with electrolytically produced hydrogen. The production of e-chemicals, such as methane through the Sabatier process, ammonia through the Haber–Bosch process, and liquid e-fuels containing longer hydrocarbon chains with the Fischer–Tropsch process, all require hydrogen as one of their precursors. Furthermore, some carbon-intensive chemical processes, such as industrial steel production, can be modified to use hydrogen for the reduction of iron ore [9]. Clean hydrogen production is, thus, a key factor enabling the electrification of a number of chemical processes that are currently highly dependent on fossil raw materials and that are responsible for producing a significant proportion of the global greenhouse gas emissions.

1.2 Methods of Hydrogen Production Hydrogen has been a useful precursor in many chemical processes even before the novel uses proposed for the green transition. Its main usage has traditionally been in oil refining, being a necessary ingredient in hydrotreaters and hydrocrackers, but also in the Haber–Bosh process for ammonia production. The current go-to method for producing hydrogen is steam methane reforming (SMR), which is a significant

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emitter of fossil carbon dioxide. In SMR, natural gas and water are converted into hydrogen and carbon dioxide. Another method for hydrogen production using fossil raw materials is coal gasification, which splits water with the help of solid carbon and oxygen (O2 ) into hydrogen and carbon monoxide (CO). Compared with the more traditional hydrogen production techniques, water electrolysis is considered more environmentally friendly as carbon dioxide emissions can be avoided completely when coupled with renewable electricity sources [10]. Water electrolysis has the advantage of producing extremely pure hydrogen, but so far, its applications have mostly been limited to small scale hydrogen production [11]. Currently, only 4% of the world’s hydrogen is produced using water electrolysis, the rest being generated with SMR, coal gasification, or as a side product of oil refining [12]. In recent years, however, water electrolysis has become a popular option for hydrogen production, mostly because its ability to produce carbon neutral hydrogen. To differentiate hydrogen based on the climatic impact of its production methodology, a colour classification has been developed. With this classification, the hydrogen produced through SMR and coal gasification is referred as gray and black/brown hydrogen, respectively, when the released carbon dioxide is not captured, and blue hydrogen when the carbon dioxide is captured and stored out of use. Hydrogen produced through electrolysis using fully renewable sources is referred to as green hydrogen. Other colors of hydrogen than the ones presented here have also been proposed, but by the time of writing, their usage has not become fully established yet. In water electrolysis, electric current is directly used for splitting water molecules to produce hydrogen according to Faraday’s law of electrolysis:   i cell Aeff . n˙ H2 = Nc ηF zF

(1)

The law simply states that the rate of an electrochemical reaction is directly proportional to the applied current, depicted here as the product of current density, i cell , and effective cell area, Aeff . The symbol z is a stoichiometric constant denoting the number of electrons required for the reaction to produce a single molecule of product, and Faraday’s constant, F, converts that number into the amount of charge needed in coulombs. In a stack of Nc cells in series, the same current flows through all the cells producing the same amount of products in each one. The Faraday efficiency, ηF , is defined as the percentage of the supplied electric charge that is successfully converted into the desired chemical product. Often in water electrolysis, the Faraday efficiency is defined to be one, as there is no significant electrochemical side reactions. Other losses of current can, however, also exist in the system, resulting in a current efficiency lower than one. Most notable of these losses are shunt or leakage currents bypassing some of the cells in a stack, and gas diffusion through the cell separator [13, 14]. Although the hydrogen production from water electrolysis is directly proportional to the applied current, the power consumption depends also on the system voltage. The voltage over an electrolytic cell, Uc , itself is a nonlinear function of the current

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1.8 1.6 1.4 1.2 1 0.8 0.6 0.4

Concentration overpotential Activation overpotential Ohmic overpotential Reversible potential

0.2 0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fig. 1 Separated contributions of each of the potential terms for an arbitrary water electrolyzer polarization curve. The figure axes are drawn on a per-unit -basis, where voltage and current units are represented compared to the reversible potential and the maximum current limited by the concentration overpotential, respectively

applied to it. A plot of the voltage characteristics as a function of current is often called the I–V curve, the U–I curve, or the polarization curve. The cell voltage can be described as a sum of several distinct terms: Uc = Urev + Uohm + Uact + Ucon .

(2)

The contributions of each of these terms for an arbitrary water electrolyzer polarization curve are illustrated in Fig. 1. Reversible potential, Urev , is the fundamental electrochemical potential required for the specific electrochemical reaction. It is independent of current, but depends on temperature, pressure, and reagent and product concentrations in the system. The rest of the terms in Eq. (2) are overpotentials, which are responsible for power loss in the electrolysis process. Overpotential caused by ohmic resistance, Uohm , is linearly dependent on the applied current, according to Ohm’s law. With careful modelling, the ohmic losses of different parts of the cell, such as the separator, electrolyte, and contact surfaces, can be separated from each other. Activation overpotential, Uact , expresses the voltage needed to overcome the kinetic barrier of the reaction. It is often modeled using the Butler–Volmer equation or one of its approximations, such as the Tafel equation, which is valid for high enough current densities. Activation overpotential is a non-linear function of current density behaving nearly logarithmically. Concentration overpotential, Ucon , represents the additional voltage caused by insufficient replenishing of reagents, in this

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A. Ibáñez-Rioja et al.

case water, on the reaction site when a very high current density is applied. Its effect is mostly unnoticed in water electrolysis, because the current densities used are seldom high enough. Therefore, concentration overpotential is often omitted from the modelling of water electrolyzers. A more detailed description of the modelling of these overpotentials for water electrolysis is presented for example in [15]. One of the main performance indicators of a water electrolyzer system is the specific energy consumption (SEC) defined as the total energy required to produce a kilogram of hydrogen:  t1

 t1

S EC =

t Ps dt  t10 ˙ H2 dt t0 m

t

Is Us dt

t0

m˙ H2 dt

=  0t1

,

(3)

where Ps is the instantaneous power of the system, which is calculated as a product of instantaneous current, Is , and voltage, Us . Mass flow of hydrogen, m˙ H2 , on the other hand, can be, in theory, calculated based on the molecular production rate, n˙ H2 , from Eq. (1) by multiplying it with the molecular mass of hydrogen. In reality, however, the mass flow should be measured from the system, as effects like diffusion of gases to the opposite electrode [14] and shunt currents bypassing certain cells in a stack [13] can cause the actual hydrogen production to be significantly lower than what would be predicted according to Faraday’s law of electrolysis. Care must also be taken when integrating over the instantaneous power Ps in Eq. (3) as non-linearity in the polarization characteristics of the electrolyzer causes a difference between the mean and root mean square (rms) values of the voltage in the case of a fluctuating current supply [16].

1.3 Water Electrolysis Technologies Since Troostwijk and Diemann first observed the electrolysis phenomena in 1789 [17], from early industrial alkaline water electrolyzers of 1900, to today’s large-scale industrial installations, water electrolysis technologies have been continuously developing and advancing. Currently, a variety of water electrolysis technologies exist in the market. The most common of these are alkaline water electrolysis (AWE), proton exchange membrane/polymer electrolyte membrane water electrolysis (PEMWE) and solid oxide water electrolysis (SOWE). Although the technologies have different electrolytes and electrochemical half-reactions, the end product is the same [18]. In terms of development and industrial adoption, alkaline and PEM electrolyzers are regarded as the most advanced technologies.

Off-Grid Green Hydrogen Production Systems

1.3.1

45

Alkaline Water Electrolyzer

Hydrogen production using AWE has developed into a reliable method for green hydrogen production. Today, AWEs have reached megawatt scale and by being the simplest and most mature technology, they now account for most industrial water electrolyzer installations worldwide [19]. AWE uses a liquid electrolyte made out of potassium hydroxide (KOH) or a sodium hydroxide (NaOH) water solution. Liquid electrolyte is used to increase ionic conductivity compared to pure water at operating temperatures of 70-90◦ C, which enables current flow through the liquid. Two electrodes are immersed in the electrolyte and are separated by a diaphragm, which keeps the product gases apart from each other, increasing the safety and efficiency of the system while still allowing hydroxide ions (OH− ) and water molecules to permeate. The basic working principle of AWE cell is presented in Fig. 2 while Table 1 lists the half-cell reactions and the total reaction. Electrodes which are submerged in the liquid electrolyte are supplied with direct current (DC) which provides the energy needed for the electrochemical reactions. On the cathode side, water is reduced to hydrogen and hydroxide ions; these ions pass through the diaphragm to the anode, where they react to form water and oxygen. Alkaline water electrolyzer technology is already well established and has been researched the longest out of the industrial water electrolyzer technologies. Adoption of AWE technology has been driven by the simple design and low cost compared

Fig. 2 Alkaline water electrolyzer working principle [20]

Table 1 Alkaline water electrolyzer chemical reactions

Reaction Anode Cathode Total

2OH− → 21 O2 + H2 O + 2e− 2H2 O + 2e− → 2OH− + H2 H2 O → H2 + 21 O2

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to other available electrolyzer technologies. The costs of production is low because it is possible to use low-cost, non-noble catalysts made of nickel-based materials in AWE. Low partial load range, and low current density still present limitations to the operation and efficiency of AWE. The use of a liquid electrolyte also increases the volume of the electrolyzer and introduces inertia in the operation, which makes AWEs unable to react to fast input power changes [21]. Currently, issues also arise from the use of bipolar configuration in AWE, which, compared to unipolar, has lower ohmic losses, but at the same time is much more susceptible to shunt currents [22, 23]. The downsides are still outshone by the fact that AWEs don’t require noble metal catalysts, which makes the technology immune to possible supply issues of those metals in the future. In general, AWEs offer a straight forward technology to scale green hydrogen production to large industrial scale needed for the future energy system.

1.3.2

Proton Exchange Membrane Water Electrolyzer

The beginning for PEMWE was in the 60s, when General Electric developed a fuel cell system based on a solid electrolyte concept for NASAs gemini program [24]. For the electrolyte, General Electric chose solid sulfonated polystyrene membrane. In the late 60s, a new membrane called Nafion® was adapted for use in the PEM fuel cells, which would become the most widely used membrane for PEM water electrolyzers and fuel cells [25]. In addition to Nafion® other options such as fumapem® and Aciplex® also exist, all of these being perfluorinated sulfonic acid ionomers like Nafion® . Thin membrane thickness (around 100 µm) is in part the reason for many advantages of the proton exchange membrane [21]. The basic working principle of PEMWE is shown in Fig. 3 while Table 2 lists the half reactions and the total reaction. On the anode side, water is reduced to oxygen and hydrogen ions; the ions pass through the membrane to the cathode, where they combine to form hydrogen gas. PEM water electrolyzers have many advantages compared to alkaline systems. In PEMWE ohmic losses are greatly reduced compared to AWEs as the thin membrane is capable of providing good proton conductivity (0.1 ± 0.02 S cm−1 ) [26] making higher current densities possible. PEMWE produce high purity hydrogen (99.999%) as the gas crossover rate is low, and this allows PEM electrolyzers to work in a wide operation range, typically 10–100% of the nominal power. PEMWE can respond quickly to the power input, not delayed by inertia of the liquid electrolyte, allowing dynamic operation even under extreme power transients on second-to-second timescale [27]. The solid electrolyte used in PEMWE also means that the electrolyzer structure can be built smaller. Additionally, with the strong structural properties of the solid electrolyte, high operational pressures (equal or differential across the membrane) are achievable. Producing hydrogen at high pressures eliminates the need for an external compressor, thus reducing the complexity of the whole system and the levelized cost of hydrogen [28].

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Fig. 3 PEM water electrolyzer working principle [20]

Table 2 PEM water electrolyzer chemical reactions

Reaction Anode Cathode Total

H2 O → 2H+ + 21 O2 + 2e− 2H+ + 2e− → H2 H2 O → H2 + 21 O2

On the other hand, most prominent drawbacks of PEMWE systems is the need for noble metals such as iridium, titanium, and platinum which increase the price of the electrolyzer. Iridium is seen as a large bottleneck for PEMWE manufacturing; it is one of the rarest elements in Earth’s crust, with yearly production of only around 3 tonnes. Reduction of noble metal loading in the catalyst is an especially important area of study for PEMWE, as with the current loadings, scaling up the production of PEMWE would not be feasible [29]. Currently, PEM water electrolyzers are commercially available but at the same time, major drawbacks still exist. Limited material selection will limit the scalability of the technology if those materials cannot be replaced with more common ones or the loadings are not significantly reduced [30]. Research and modelling to investigate PEMWE’s degradation and dynamic response are still in their early stages and further work is required [31–33].

1.3.3

Solid Oxide Electrolyzer

Research into solid oxide water electrolyzers has its beginning in 1975 when project HOT ELLY was started in Germany [34]. A decade later, Dönitz and Erdle introduced the first functional water electrolyzer using solid oxide electrolyte cells (SOECs) [35]. Running at, 1000 ◦C the system was able to run at constant current density of 0.3 A cm−2 at a voltage of 1.07 V during a 1000 h test run without significant voltage change. Increasing the operating temperature of the electrolyzer increases

48 Table 3 Solid oxide electrolysis chemical reactions

A. Ibáñez-Rioja et al. Reaction Anode Cathode Total

O2 → 21 O2 + 2e− H2 O(g) + 2e− → H2 + O2− H2 O(g) → H2 + 21 O2

Fig. 4 SOEC working principle [20]

the total energy demand slightly, while the electrical energy needed decreases significantly. Thus, increasing the theoretical maximum for electrolyzer efficiency [36]. Although SOECs show a lot of promise, they are still under development and have only seen very limited adoption in industry. Currently, SOEC research is very active, and numerous researchers from companies, research centres, and universities around the world are contributing to it. Thus, SOECs have seen fast advances in technology and are approaching the one megawatt scale. In SOEC water in the form of steam is fed to the cathode, where the water is split into hydrogen and oxide anions according to Table 3. The oxide anions produced in the cathode pass through the solid electrolyte to the anode, where they recombine, forming oxygen. The working principle and basic components of SOEC are presented in Fig. 4. The cathode is made of a cermet material, usually consisting of nickel and yttria (Y2 O3 )-stabilized zirconia (ZrO2 ), shortened as YSZ. The anode is commonly a composite of YSZ and perovskites such as ferrites (LaFeO3 ), lanthanum manganites (LaMnO3 ) or cobaltites (LaCoO3 ) partially substituted with strontium to improve structural and electronic defects which increase electrocatalytic activity [37, 38]. 8 mol % YSZ has been extensively used as an electrolyte in solid oxide fuel cells [39]. It has high oxygen-ion conductivity while also acting as an electronic insulator when operating at high temperature (∼1000 ◦C) [40]. Zirconia is used because of its high melting point, good mechanical strength, and excellent corrosion resistance.

Off-Grid Green Hydrogen Production Systems Table 4 Co-electrolysis chemical reactions

49 Reaction

Cathode Cathode Anode Total

H2O + 2e− → H2 + O2− CO2 + 2e− → CO + O2− 2O2− → O2 + 4e− H2O + CO2 → H2 + CO + O2

Although operating at high temperatures has benefits for cell efficiency and reduced voltages, it causes issues with material durability and sealing of the electrolyzer. Currently, material degradation presents one of the major issues in SOECs [41, 42]. Additionally, processing of the hydrogen gas is complicated by the fact that it is mixed with steam when leaving the cathode side outlet. The more complicated separation process increases the capital cost of using SOE water electrolysis compared to competing water electrolyzer technologies such as AWE and PEMWE. These issues have to be tackled for wider adoption of the technology to take place. Due to the high operation temperature of the SOEC it is also possible to produce syngas (H2 + CO) with electrolysis of both water and carbon dioxide simultaneously [43]. This operation mode is commonly called co-electrolysis and the resulting syngas can be further refined to produce synthetic fuels using Fischer-Tropsch conversion [44]. The electrochemical reactions in co-electrolysis are shown in Table 4. In addition to these reactions water gas shift reaction (WGS) can also occur in parallel to the reactions listed in Table 4: H2 O + CO  H2 + CO2 .

(4)

At temperatures above 820 ◦C the reaction balance of the WGS is shifted towards the production of carbon monoxide and water which is called the reverse water gas shift reaction (RWGS) [45, 46]. Thus, increasing the production of carbon monoxide at the expense of reduced hydrogen content.

1.3.4

Anion Exchange Membrane Electrolyzer

Anion Exchange Membrane Water Electrolyzer (AEMWEs) operate in an alkaline environment, allowing non-noble catalyst materials to be used. In AEMWEs the conventional diaphragm is replaced with an Anion Exchange Membrane (AEM) which lets hydroxide ions and water to pass through but prevents gasses from grossing. The general idea is to implement the best parts of AWE and PEMWE in one water electrolyzer technology, which is made possible using the AEM [47]. Compared to alkaline electrolyzers, AEMWEs offer higher current densities and a larger operating range, while still being able to operate under an alkaline environment. Additionally, it is possible to run AEMWEs with much lower electrolyte concentrations than traditional AWE, and work is currently being done to use only deionized water [48]. Of

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A. Ibáñez-Rioja et al.

Fig. 5 Working principle and the components of anion exchange membrane electrolyzer [20]

Table 5 Anion exchange water electrolyzer chemical reactions

Reaction Anode Cathode Total

2OH− → 21 O2 + H2 O + 2e− 2H2 O + 2e− → 2OH− + H2 H2 O → H2 + 21 O2

the three low temperature water electrolyzer technologies, AEMWEs are the leastmature, and significant technical hurdles still exist especially in terms of membrane development [49]. As water is able to diffuse through the AEM, it is possible to run the AEMWE with just anode side water supply even though performance can be further improved by circulating water on both sides [50]. The components of AEMWE are illustrated in Fig. 5 and the electrochemical reactions happening at anode and cathode are presented in Table 5. On the cathode, the water is split into hydrogen and hydroxide ions by addition of two electrons. The hydroxide ions then pass through the AEM to the anode side, where the ions react to form oxygen and water. In terms of chemical reactions AEM and traditional alkaline electrolyzers work identically but the usage of AEM changes the working principle. Using non-noble metals as the cathode and anode catalysts gives AEMWEs the same benefits as AWEs. AEMWEs same as AWEs, use nickel-based catalyst materials for anode and cathode as these materials are very cost-effective and abundant [51, 52]. While for the AEM the most used membranes are Commercial Fumatech FAA-series (Fumatech corp., Germany), Sustainion (Dioxide Material Corp., USA), and Tokuyama A-201 (Tokuyama Corporation, Japan) [48, 53]. The most significant issue with AEMWEs is the low maturity of the technology, as it has not seen the same kind of research interest as PEMWE and AWE. As such, many issues are still present with the AEMWE systems, with a major one being the high degradation of the AEM. In general most AEMs are prone to low chemical stability especially at elevated temperatures, which is mainly caused by

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51

displacement of the ammonium group by the hydroxide ions [54]. Furthermore, in the current development stage, performance is still lower when compared to PEMWE due to the higher internal resistance of the membrane electrode assembly [48]. Although many studies have reported good potential in AEMWE technology, the technology is still in research and development stage. Development is still needed, especially in optimization of the materials for increased performance and lifetime. Further tests with laboratory-scale demonstration units have to be performed to improve the materials and get AEMWE to industrial scale [12, 55].

1.4 Green Hydrogen and Smart Grid Integration Hydrogen as an energy carrier can play a significant role in smart grid systems. The term “smart grid” has been used for applications mainly involving the electricity sector, and therefore, more accurate terminology is now required when hydrogen is integrated into the system. A new paradigm is emerging in which multiple sectors and infrastructures are integrated to benefit from one another. The term “smart energy system” is then used to define this concept more accurately [56]. Figure 6 presents a generic scheme of a green hydrogen system integrated into a smart energy system. Renewable power sources, such as wind and solar photovoltaic (PV), are used to supply the electricity required to produce hydrogen in the electrolyzer plant. The hydrogen can be pumped through the gas grid, which can be connected to other producers and consumers within the smart energy grid. Therefore, the gas grid itself can be considered an element of the smart energy grid. The surplus electricity production is supplied to the electricity grid, which, in turn, is part of the smart energy grid. A battery energy system is included to provide steadier power supply to the electrolyzer, which also enhances the durability of the electrolytic cells. The total solar PV peak power installed, the nominal wind power deployed, and the battery capacity are variables that need to be optimized in addition to the operation control of the system to ensure the best plant performance and minimize the hydrogen production costs [57]. Intermittent renewable power generation presents a challenge for the stability of the electricity grid and the supply–demand balance. Hydrogen could have an important function to mitigate those effects. The scheme presented in Fig. 6 includes a hydrogen storage, which operates as a buffer tank and supplies hydrogen to any consumer throughout the gas grid. Besides that, from the electricity point of view, a fuel cell plant can supply renewable power to the smart energy grid using the hydrogen that was previously stored. In the event of surplus renewable electricity generation, when it cannot be delivered into the electricity grid, the electrolyzer plant can produce hydrogen and store it for a later use. In the same manner, when there is a peak of electricity demand from the grid, stored hydrogen can be used in the fuel cell plant to balance the electricity grid.

52

A. Ibáñez-Rioja et al. Electricity Grid

Renewable Electricity

Surplus electricity production

Smart Energy System

Fuel cell Plant

Gas Grid End-use Wind power

Electrolyzer Plant Solar PV power

Green Hydrogen Production

Hydrogen Storage

Battery Energy System

Fig. 6 Integration of a green hydrogen system into a smart energy system. Because hydrogen and not just electricity are incorporated into the smart energy system, the term smart energy system is used in place of the term smart grid

In order to ensure renewable electricity production in the fuel cell plant, the hydrogen to be supplied must have been produced with a renewable power source, and therefore, the fuel plant is directly connected to the hydrogen storage and not to the gas grid, in which the “color” of the hydrogen might not be ensured. The principle of fuel cells is, in essence, the opposite to the electrolyzers. In fuel cells, the electrochemical cells convert the chemical energy of hydrogen into electrical energy. The energy conversion process has a higher efficiency than other conventional technologies with significantly lower greenhouse gas emissions [58]. Because of the low density of hydrogen gas and tendency to escape containers due to small size and reactivity with metals and polymers, its storage represents a challenging task. Various technologies have been developed and are currently being investigated, with the main goal of increasing the storage density. For compressed hydrogen gas, pipe storage is the most viable solution for the large-scale use, allowing high storage pressures of approximately 100 bar [59]. Therefore, the gas grid of the smart energy system presented in Fig. 6 could be used not only for transporting hydrogen but also as a storage method.

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2 Dynamic Modelling and Analysis of an Industrial Alkaline Water Electrolyzer Plant In this section, a dynamic energy and mass balance modelling approach and analysis of a 3 MW capacity and 16 bar pressure industrial-scale alkaline water electrolyzer (AWE) plant process is elaborated on. The industrial process is modelled in a Simulink environment with integrated MATLAB system blocks, where the semiempirical mathematical models and balance equations are solved by object-oriented programming. Each subsection provides information on the chosen technologies, modelling approaches and balance equations of most unit operations.

2.1 Alkaline Water Electrolyzer Plant Process Overview Figure 7 shows a typical industrial AWE plant process. The plant consists of the electrolyzer stack and the peripheral supporting components, namely: (1) a power supply system; (2) horizontal gas-liquid separation vessels; (3) centrifugal pumps; (4) shell-and-tube heat exchangers; (5) an optional mixing scheme; (6) an intermediate storage tank; and (7) a hydrogen purification system [60]. Industrial-scale AWE stacks require hundreds of volts of DC voltage and thousands of amperes of DC current [61], with minimum AC ripple, to achieve higher electrolyzer efficiencies [62]. For larger-scale applications, these systems usually

Flow Hydrogen Oxygen Electrolyte Feedwater

H2-Liquid Separation

AC Source

H2 Buffer Tank

99,999 H2 dry [%]

H2 Demister H2 O Drainage

Transformer CWout 6-pulse rectifier

O2 Vent

CWin

H2 Cooler

DC O2-Liquid Separation

H2 Purification System

H2 + Electrolyte

O2 + Electrolyte

Anode Side Electrolyte Circulation

Alkaline water electrolysis stack

Cathode Side Electrolyte Circulation

Centrifugal Pump

Mixer CWin

CWout

Agitation Process / Piping

H2O [kg/h]

Deionizer + Water storage + Feedwater

Lye Cooler

Lye Cooler Centrifugal Pump

H2O treatment system

H2-Liquid Separation

CWin CWout

Fig. 7 An example of a typical alkaline water electrolyzer plant process diagram

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consist of transformers to step down the voltage level and large current rectifiers in combination with filters to convert the supplied AC to DC and improve the power quality. The AWE stack receives the DC current, and converts the water into hydrogen molecules in the catalyst plane of the cathode compartment, and oxygen molecules in the catalyst surface of the anode compartment by water splitting reaction as shown in Table 1. The two-phase flow of electrolyte solution and produced gas leave the stack and enter the horizontal gas-liquid separation vessel, where the gas separates from the electrolyte liquid by gravitational force. Next, a condenser cools the separated hydrogen gas until dew-point is reached to remove part of the moisture. A buffer tank then intermediately stores the hydrogen and releases it at time intervals into the purification system to eliminate the remaining moisture and oxygen impurities from the output hydrogen gas. Thus, the produced hydrogen is of very high purity. On the anode circulation side, the produced oxygen gas typically undergo the same process as the hydrogen, but for simplification of the study, it is vented out. At the bottom of the horizontal gas-liquid separation vessels, the separated electrolyte solution recirculates back into the AWE stack with the aid of centrifugal pumps. Before entering the stack, shell-and-tube heat exchangers cool the electrolyte solution to balance and control the temperature of the stack at the desirable operational conditions. In addition, occasional mixing of the electrolyte before the stack is necessary to maintain the concentration of potassium hydroxide the same between the cathode and anode. The concentration may vary due to the yield of water on the anode compartment and consumption at the cathode compartment.

2.2 Alkaline Water Electrolyzer Stack Figure 8a shows a common bipolar AWE stack. The stack consists of multiple electrochemical cells connected in series. The foremost end plates serve as current collectors and maintain the structure, usually with the help of tie rods. In a bipolar stack structure, the electrode (Fig. 8b, 4) splits two cells and functions as an anode in one cell and as a cathode for the next one. In turn, the separator diaphragm (Fig. 8b, 3) acts as a sealing gasket and prevents the hydrogen produced at the cathode compartment to mix into the oxygen at the anode compartment, and vice versa [63]. Another configuration option is the unipolar, where the stack comprises electrochemical cells in a parallel connection. Nevertheless, the bipolar configuration is commonly selected for industrial applications because of its compact design and higher achievable current density [63]. However, in bipolar configurations the high conductive electrolyte, which circulates through the connective manifold scheme, can cause shunt currents and corrosion [64]. Figure 8b shows one cell of a common bipolar style AWE stack. A generic structure of the cell includes the following components: 1. Anode-side space for electrolyte flow 2. Cathode-side space for electrolyte flow

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Fig. 8 Schematic of a common bipolar alkaline water electrolysis stack and b one cell schematic

3. 4. 5. 6. 7. 8. 9. 10.

Separator diaphragm/membrane Bipolar plate (electrode) Anode catalyst plane of the neighbor cell Cathode catalyst plane Cathode catalyst plane of the neighbor cell Anode catalyst plane Inlet manifold pipe Outlet manifold pipe.

2.2.1

Electrochemical Model

The electrochemical model consists of several empirical correlations to describe the cell voltage as a function of current density, temperature, and pressure and is based on the Ulleberg’s model [65]. The reversible cell voltage, as explained in Sect. 1.2, expresses the minimum electrical energy required for the water splitting reaction to occur and can be obtained from the Nernst equation as [66]:

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Urev

  (P − Pv,KOH ) (P − Pv,KOH )0.5 ) RT , ln = Urev,0 + zF aw,ele

(5)

where T and P express the temperature and the pressure in which the electrochemical reaction takes place, and R is the universal gas constant. Urev,0 is the standard equilibrium potential and can be empirically estimated from Eq. (6), based on [67]: Urev,0 = 1.5184 − 1.5421.10−3 · T + 9.526.10−5 · T · lnT + 9.84.10−8 · T 2 . (6) The vapor pressure of the solution, Pv,KOH , is calculated as shown in Eq. (7): b Pv,KOH = 10a · Pv,w

(7)

where a (Eq. 8) and b (Eq. 9) are experimental coefficients, originally found from [68]. a = −0.0151 · M − 1.6788.10−3 · M 2 + 2.2588.10−5 · M 3 ,

(8)

b = 1 − 1.2062.10−3 · M + 5.6024.10−4 · M 2 − 7.8228.10−6 · M 3

(9)

In addition, the saturated water vapor pressure Pv,w can be obtained from the Antoine equation Eq. (10). Pv,w = 105.1962−(1730.63)/(−39.724+T ) .

(10)

The water activity in potassium hydroxide is based on [68]; it is a function of the molality and temperature of the solution and can be estimated as shown in Eq. (11).   (3.177 · M − 2.131 · M 2 ) . aw,ele = exp −0.05192 · M + 0.003302 · M 2 + T (11) The ohmic overpotential Uohm indicates the voltage drop between the anode and cathode electrodes. This voltage drop arises owing to the resistance of the bipolar plates, electrodes, current collectors, and their interconnections to the electron flux, as well as the resistance of electrolyte and membrane/diaphragm to the transport of ion flux [66]. The ohmic overpotential is proportional to the current supply and it increases linearly with higher current densities. Based on [65], Eq. (12) expresses the ohmic overpotential as a function of temperature, where α1 and α2 are fitting model parameters: (12) Uohm = α i c = (α1 + α2 T ) i c .

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The activation overpotential Uact emerges from the anode and cathode reaction kinetics or slowness of the electrochemical reactions [66], and depends on the electrode materials, electrocatalytic properties, current density, temperature, pressure, surface roughness, pH, adsorption of ions, and agitation. Based on [65], Eq. (13) expresses the activation overpotential as a function of temperature, where β1 , β2 , β3 are fitting model parameters and s describes the Tafel slope coefficient: Uact

   β3 β2 + 2 ic + 1 . = s log(β i c + 1) = s log β1 + T T

(13)

All six electrochemical model parameters (α1 , α2 , β1 , β2 , β3 , s) should be determined experimentally by fitting calculated polarization curves to the measured polarization curves. At least three curves are required at different temperatures.

2.2.2

Stack Mass Balance

Equation (14) expresses the mass balance of an AWE stack. The rate of change of mass  dm in time dt is equivalent to the total mass flow rate of the electrolyte at the inlet m˙ ele,i , subtracted by the total mass flow rate of the electrolyte at the outlet  rs . The total reaction rate m˙ ele,j , with the addition of the total reaction rate describes the overall yield of water in the stack, the production of oxygen gas in the anode half cells, and the production of hydrogen gas in the cathode half cells.     dm = m˙ ele,i − rs . m˙ ele,j + dt 2.2.3

(14)

Cell Mass Balance

Equation (15) describes the mass balance for one cell, where m˙ cat,i and m˙ an,i represent the mass flow rates at the cathode and anode inlets of a single cell, and m˙ cat,j and m˙ an,j are the mass flow rates at the cathode and anode outlets of a single cell: dm = (m˙ cat,i + m˙ an,i ) − (m˙ cat,j + m˙ an,j ). dt

(15)

The respective mass flow rates at the inlet of a single cell are obtained as shown in Eq. (16):  m˙ ele,i m˙ cat,i = m˙ an,i = . (16) 2 Nc

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The mass flow rate at the cathode outlet of a single cell is obtained as: m˙ cat,j = m˙ cat,i + m˙ H2 − m˙ w,cn ,

(17)

where m˙ H2 represents the mass flow rate of the produced hydrogen gas, and m˙ w,cn is the mass flow rate of the consumed water. The mass flow rate at the anode outlet of a single cell is estimated as: m˙ an,j = m˙ an,i + m˙ O2 + m˙ w,pd ,

(18)

where m˙ O2 serves as the mass flow rate of the produced oxygen gas, and m˙ w,pd is the mass flow rate of the yielded water.

2.2.4

Thermal Model

This model uses a lumped-capacitance method, to estimate the temperature behavior of the stack. The lumped system analysis assumes that the stack is submerged and quenched from the outer atmosphere, and the inner temperature gradients are spatially uniform. This method is justifiable because the heat convection from the control volume body to the environment is slower compared to the heat conduction within the stack [60]. Equation (19) expresses the ODE to transiently estimate the temperature of the stack. The left-hand side of the equation describes the rate of temperature change in time, where Cs is the stack’s thermal capacitance constant. On the right-hand side of the equation, the temperature of the stack increases due to the overpotential power loss Q˙ loss and the power loss caused by the shunt currents Q˙ shunt , and decreases as a result of the power loss to ambient Q˙ amb and because of the flow of liquid electrolyte which is colder as its cooled in the heat exchanger before entering the stack Q˙ liq . Cs



dT = Q˙ loss + Q˙ shunt − Q˙ liq − Q˙ amb . dt

(19)

The activation and ohmic overpotentials generate heat. Below the thermoneutral voltage, the heat accelerates the electrochemical reactions. Above the thermoneutral voltage, the heat dissipates in the stack and increases the cell/stack temperature. This overpotential power loss can be obtained from [62]: Q˙ loss = Nc (Uc − Utn ) I,

(20)

where Nc is the total number of cells, Utn is the thermoneutral voltage, and I the current supply to the stack. The heat exchangers at the anode and cathode circulation cool the recirculated electrolyte flow. This liquid flow enters the stack and removes heat from it. This cooling can be estimated as:

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Q˙ liq = m˙ ele,i Cp,ele (Ts − Tele,i ),

(21)

where m˙ ele,i is the mass flow rate of the electrolyte at the inlet of the stack, Cp,ele is the specific heat of the electrolyte solution, Tele,i expresses the temperature of the electrolyte at inlet, and Ts is the temperature of the stack. The heat loss from the stack to the outer environment is estimated as radiative Q˙ rad and free-convection Q˙ cnv transfer heat and is obtained from:

4 + h As (Ts − Tamb ), Q˙ amb = Q˙ rad + Q˙ cnv = σ As ε Ts4 − Tamb

(22)

where As expresses the outer surface area of the stack, σ is the Stefan-Boltzmann constant, ε describes the emissivity of the outer material, Tamb is the ambient temperature, and h is the average heat transfer coefficient, which can be calculated by Eq. (23), where k is the thermal conductivity of the outer material, Nu is the Nusselt number and, D is the diameter of the stack. Similar numbers can also be calculated from the correlation in Eq. (24) for natural convection of horizontal cylinders: h = Nu  h = 1.32 ·

k , D

Ts − Tamb D

(23) 0.25 .

(24)

The thermal capacitance describes the required heat to increase the temperature of the system by a unit [69]. Equation (25) shows the overall thermal capacitance constant of the stack Cs . This constant is a function of density ρi , heat capacity Ci , and volume Vi of each discrete element composing one cell in the arrangement of the total stack [62]. As explained in Sect. 2.2, a typical AWE cell contains a bipolar plate, a separator diaphragm/membrane, and two electrolyte flow channels for the anodic and cathodic faces:   (25) Cs = ρi Ci Vi Nc , Cs = (ρbi Cbi Vbi + ρmem Cmem Vmem + 2 ρele Cele Vele ) Nc ,

(26)

where subscripts bi, mem and ele symbolize the bipolar plate, the membrane and the electrolyte space respectively.

2.3 Horizontal Gas-Liquid Separation Vessel Industrial-scale AWE plants employ horizontal gas-liquid separation vessels to isolate the end-product gas from the electrolyte (liquid). Figure 9 describes the separation vessel on the cathode circulation side. The mixture flow (two-phase) enters the

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Electrolyte H2 O2,imp

Gas gravity separation section

Liquid gravity separation section

Interconnection Electrolyte flow

Makeup H2O

O2,imp H2,imp Electrolyte

Fig. 9 Horizontal gas-liquid separation vessel at the cathode circulation side

separation vessel through the feed pipe. During the gas-gravity separation section, the heavier liquid droplets separate from the hydrogen and oxygen (impurity gas) bubbles. Even though most of the bubbles successfully separate, or degas, from the liquid phase and end up in the upper right-side outlet, a small percentage of the hydrogen and oxygen bubbles dissolve in the liquid and end up in the lower right-side outlet and eventually circulate in the process scheme as impurities [70]. The gas-gravity section removes most of the liquid droplets (mist) travelling upwards in the vessel. Demisters installed at the inside or outside the separation vessel, eliminate the remaining droplets. As shown in Fig. 7, a condenser (cooler) after the separation vessel and before the demister can enhance the efficiency of the mist extraction process. A pipeline connects the cathode-side separation vessel to that of the anode-side. The interconnection pipe balances the liquid height, temperature, pressure, and potassium hydroxide concentration between the separation tanks. However, impurities in the system increase as a result of the dissolved hydrogen and oxygen crossing to the electrolyte circulation of the opposite electrode via the pipeline. Finally, a makeup feed water flow is necessary to maintain the liquid level inside the separation vessels and compensate for the water consumption during the electrochemical reactions in the stack.

2.3.1

Mass Balance

Equation (27) shows the total mass balance of the horizontal gas-liquid separation vessel at the cathode circulation side. The equation states that the rate of mass change

Off-Grid Green Hydrogen Production Systems

over time dt is equivalent to the total  mass flow rate at the inlet overall mass flow rate at the outlet m˙ j :   dm m˙ j . = m˙ i − dt

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m˙ i , minus the

(27)

Equation (28) describes the total mass flow rate at the inlet: 

m˙ i = m˙ H2 ,i + m˙ O2 ,imp,i + m˙ ele,i + m˙ m,i ,

(28)

where m˙ H2 ,i is the mass flow rate of hydrogen bubbles, m˙ O2 ,imp,i is the mass flow rate of oxygen bubbles (impurities), m˙ ele,i expresses the mass flow rate of the electrolyte feed water mass flow rate. Equation (29) describes liquid, and m˙ m,i is the makeup  the total mass flow rate m˙ j at the outlet: 



m˙ j = m˙ H2 ,i ηs,H2 + m˙ O2 ,imp, i ηs,O2 + m˙ ele,i (1 − ηsl )

+ m˙ ele,i ηsl + m˙ H2 ,i (1 − ηs,H2 ) + m˙ O2 ,imp,i (1 − ηs,O2 ) + m˙ m,i , (29)

where ηs,H2 is the separation efficiency of the hydrogen gas and ηs,O2 is the separation efficiency of the oxygen gas. Estimation of these parameters requires either empirical models of the dissolution of hydrogen and oxygen in the potassium hydroxide solution, such as [71], or fitting with measurements from a real identical process. Furthermore, n sl expresses the separation efficiency of the liquid, which, with a proper mist extractor process, can be very close to one. Equation (30) describes the mass flow rate of makeup feed-water at the inlet: m˙ m,i = (m˙ w,cn ) + (m˙ ele,j,an,up + m˙ ele,j,cat,up ),

(30)

where m˙ w,cn is the overall water consumption due to the electrochemical reaction of water splitting, m˙ ele,j,an,up and m˙ ele,j,cat,up express the total liquid that is entrained upwards in the separation vessels of the anode and cathode side circulation respectively. The influence of the interconnection pipe is not shown in Eqs. (27), (28) and (29). The pipe adds electrolyte, hydrogen, and oxygen flow to the vessel with lower liquid height and removes the flow from the vessel with higher liquid height until height balance has occurred. 2.3.2

Energy Balance

Equation (31) describes the energy balance of the horizontal gas-liquid separation vessel at the cathode side circulation: Csep

dT = Q˙ heat − Q˙ amb − Q˙ sensible − Q˙ cool . dt

(31)

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where Ct,sep is the overall thermal capacitance of the tank, Q˙ heat expresses the increase of heat due to the hot electrolyte, hydrogen and oxygen flow coming from the stack, Q˙ amb describes the heat loss from the tank to the ambient, Q˙ sensible is the heat loss exiting the vessel with electrolyte, hydrogen and oxygen flow, and Q˙ cool is the cooling of the tank due to the cold makeup feed-water flow. The horizontal gas-liquid separation vessel at the anode side circulation follows the same mass and energy balances apart from the makeup feed-water influence.

2.4 Shell-and-Tube Heat Exchanger Industrial-scale AWE plants typically use recuperative shell-and-tube heat exchangers to control and maintain the temperature of the stack in optimal thermodynamic conditions. As explained in Sect. 2.2.4, the stack temperature increases as a result of the power loss caused by the overpotentials and the shunt currents. An elevated temperature favors the reaction kinetics. However, at the same time higher temperature accelerates the corrosion of the mechanical structure. To simplify the calculations, one shell and one tube pass model is assumed, where the flow between the hot and cold tubes is in parallel. Such models are solved via the first law of thermodynamics [72]. Thus, the energy balance is obtained as: q = m˙ h Ch (Thi − Thj ) = m˙ cd Ccd (Tcdj − Tcdi ),

(32)

where m˙ h and m˙ cd describe the mass flow rate of the electrolyte and cooling water, Ch and Ccd express the heat capacity rate of the electrolyte and cooling water, Thi and Thj are the temperatures of the electrolyte at the inlet and outlet of the heat exchanger, and Tcdi and Tcdj are the temperatures of the cooling water at the inlet and outlet. Proportional-integral (PI) or proportional-integral-derivative (PID) controllers maintain the temperature of the electrolyte at the outlet of the heat exchanger at desired set-point. The controller receives the signal temperature of the electrolyte flow and accordingly changes the inlet mass flow rate of the cooling water by electronically adjusting the valve position. This action retains the electrolyte temperature at the inlet of the stack at constant values.

2.5 Centrifugal Pump Centrifugal pumps with three-phase motors recirculate the electrolyte liquid back to the electrolyzer stack. The pumps ensure that the mass flow rate of the electrolyte is adequate to remove the produced hydrogen and oxygen bubbles from the electrochemical stack. At a low liquid mass flow rate, the produced bubbles tend to accumulate and stick in the electrode plane creating gas layers, which deactivate the

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electrode area, increase the ohmic resistance, and hinder the water splitting reaction, resulting in a lower hydrogen and oxygen production [73]. In addition, the pumps control the thermal behavior of the stack. At a high recirculation liquid mass flow rate, the temperature of the stack decreases because of the higher values of Q˙ liq , presented in Eq. (21), and vice versa at a low liquid mass flow rate.

2.6 Purification System The hydrogen purification scheme essentially incorporates a deoxidizer vessel, a horizontal gas-liquid separation vessel, and an adsorption system. The incoming hydrogen gas with oxygen impurities enters the deoxidizer, where most of the oxygen impurities react with hydrogen molecules to form water. In continuity, the moist hydrogen gas is then directed into a small horizontal gas-liquid separation vessel to reduce the water content. Finally, the hydrogen gas is sent to the drying process to eliminate all the moisture, resulting in a pure hydrogen flow, ready to be compressed and stored.

2.6.1

Deoxidizer

The oxygen removal occurs in an aluminum-based palladium deoxidizer. The deoxidizer chemical reactor contains a u-shaped pipe wherein the hydrogen and oxygen gas molecules contact the palladium surface, initiating the reaction of water recombination: (33) 2H2 (g) + O2 (g) → 2H2 O(g) + 244.9 kJ mol−1 . In industrial applications, the oxygen in hydrogen concentration at the outlet is lower than 0.1 ppm. Because the reaction is exothermic, the hydrogen feed from the intermediate buffer tank controls the temperature of the chemical reactor, which is usually around 130–150 ◦ C.

2.6.2

Adsorption System

The purpose of the adsorption process is to eliminate the moisture content from the gas. This scheme usually consists of two fixed beds, one operating in the adsorption phase and one in the regeneration phase [74], where highly porous solid granules (such as Zeolite 13X) fill the fixed beds. In the adsorption phase, the product gas with moisture continuously passes through the bed, and the water vapor is adsorbed in the surface layers of the granules until the granules are nearly saturated. Then, the flow is switched to the second bed, and the bed that was saturated is regenerated. In industrial applications, the phases are

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automatically switched at a scheduled time or when the accumulation of moisture in the exit fluid reaches a certain limit. In the regeneration phase, hot gasses passing through the bed or electric heaters installed inside the bed heat up the fixed bed. The higher temperatures provide the energy for desorption, and the moisture is released from the surface of the granules into the hot gas flow. Then, the moisture is vented out or removed in a condenser.

2.7 Dynamic Plant Process Modelling Methodology The plant process is dynamically modelled as shown in Fig. 10. The transient process model is based on semi-empirical mathematical models and on the solution of the energy and mass balance of each unit operation. Each of the components has its own system block. The system blocks are solved by object-oriented programming in MATLAB and are connected to each other in MATLAB Simulink. Each MATLAB system block receives certain data signals as the input and produces a definite number of data signals as the output, on a time step basis. These data signals contain the basic thermodynamic properties of the hydrogen, oxygen, and electrolyte flow, and they are transferred from one unit operation to the next one. The DC current supplied to the stack, in each time step, is the main transient force of the entire process [60].

Fig. 10 MATLAB Simulink diagram and process model methodology, adapted from [60]

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The dynamic process model is validated with real industrial data taken from a 3 MW alkaline water electrolyzer plant. The validation and results can be found in publication [60].

3 Off-Grid Alkaline Water Electrolyzer Systems In Sect. 1, the role of green hydrogen in the energy sector and its integration into smart energy systems were introduced. Section 2 described the model of an industrial alkaline water electrolyzer plant that was validated with real data. This chapter concludes by presenting the simulation results of an off-grid industrial alkaline water electrolyzer. The studied system showed in Fig. 11 is consisting of a solar PV installation, an on-shore wind farm, an alkaline water electrolyzer and a battery energy storage system (BESS). The produced hydrogen is assumed to be purified and ready to be supplied to an end user. In the framework presented in Fig. 6 of a smart energy system, the hydrogen can be pumped to the gas grid or stored. For the following analysis, the surplus electricity production is assumed to be redirected to the electricity grid. A finite-state machine controller defines the energy distribution in each time step during the simulation. The component capacities and control of the system are optimized simultaneously to minimize the cost of hydrogen production by following the simulation methodology presented in [57]. The plant simulation is based on real solar PV and wind power production data collected during the year 2021 from

Surplus electricity production

Grid

Wind power

Control System Solar PV power

Electrolyzer Plant

End-use

Green Hydrogen Production

Battery Energy System

Fig. 11 Configuration of an off-grid green hydrogen production plant. Surplus electricity production from solar PV and wind is assumed to be redirected to a electricity grid

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Fig. 12 Optimal capacity for the components (a) and (b) average annual FLHs during the 30 years simulation

installations located in southeastern Finland. By replicating the annual data, the total simulation time is 30 years with a 5 min time-step resolution. The simulation includes the degradation of the electrolytic cells, the photovoltaic cells, and the lithium ion batteries. The bar chart in Fig. 12a shows the optimal capacity of each component. The installation prices of each component are obtained from [75] and estimated prices for year 2040 are used in the simulation. Component prices for earlier years yields into an optimal system configuration with no solar PV nor battery, therefore prices of year 2040 are used to demonstrate the operation of the system when all the components are installed. In the optimized system, for a fixed 100 MW nominal alkaline water electrolyzer, the optimal solar PV peak power capacity is 125.4 MW, the nominal wind power is 121.7 MW, and a BESS of 41.9 MWh energy capacity. Full load hours (FLHs) is a measure of the utilization of certain installation or equipment and it is defined as the maximum annual energy output, or consumed energy in the case of the electrolyzer, divided by its nominal power. Currently there are significant geographical differences in hydrogen production cost, and its economic potential is dependent on elements that will continue changing, such as price of the fossil fuels and electricity. Water electrolysis requires 3000 to 6000 FLHs to become cost-competitive when compared to hydrogen produced with steam methane reforming (with carbon capture) [76]. Bar chart in Fig. 12b shows that average FLHs for the alkaline electrolyzer in the simulated case are 4304 h per year. In this case, FLHs of the solar PV installation are significantly lower than the wind power production due essentially to the low solar PV production during the Finnish winter. Despite the intermittency of the electricity production, hydrogen production in the electrolyzer plant can be optimized to perform under these conditions. Figure 13 shows 25 h operation time of the total 30 years simulated. The battery is used to maximize and stabilize the power supplied to the electrolyzer, and it is charged when the solar PV and wind power are unable to maintain the electrolyzer running at its minimum load, which was fixed to 20 % of its nominal load. The electrolyzer is using

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Fig. 13 Excerpt of 25 h from the 30 years simulation of the plant. Time-step resolution is 5 min

the battery to increase the production by operating with all the energy available in the system. Approximately between the hours 2935 and 2936, it can be observed that the electrolyzer is at maximum load while the battery is being charged simultaneously. Once the battery is fully charged around hour 2937, there is a surplus of electricity production. It is clear that the production of hydrogen in an off-grid system is strongly variable for short time intervals and fully dependent on the climate conditions in where it has been installed. However, for longer time periods the total hydrogen production can be estimated more accurately. Figure 14 shows the percentage of the surplus production and the hydrogen production for each simulated year. The hydrogen production decreases gradually during the first 13 years due to the degradation of the electrolyzer cells, after that, there is a replacement taking place in year 14. Similar behaviour can be seen in year 27. Degradation of the solar PV cells explains the decrease of surplus production during the 30 years, as the PV cells are not replaced in the simulation. On the other hand, the gradual increases of the surplus between years 2–11, 13–24 and 25–30 are caused by the capacity and power fade of the battery due to the degradation in operation [77]. In years 11–13 and 23–25 there are abrupt reductions in the surplus due to the battery replacements. Off-grid green hydrogen production via water electrolysis integrated with solar PV and wind power generator presents certain challenges mainly caused by the intermittence of the electricity production. The ramp-up of the plant can last up to 1 h until the plant reaches its nominal production capacity, depending on the initial stack temperature [60]. Hydrogen production can be stabilized if a battery is installed in the system, however to further improve the supply stability and meet the requirements of certain consumers from energy and industry sectors, hydrogen storage is needed.

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Fig. 14 Percentage of the annual surplus energy divided by the total electricity generation from solar PV and wind (left axis). Annual hydrogen production (right axis)

In a smart energy system made up of gas and electricity grids, consumers and producers can interact to benefit from each other. For instance, the surplus electricity production in the previous simulation, which represents around 7.2 % of the total annual electricity production of the plant, could be redirected to the electricity grid and used by another consumer or external energy storage operator in the smart energy system. In the same manner, the end-use of the produced hydrogen illustrated in Fig. 11 could be the gas-grid or any other hydrogen consumer within the smart energy system.

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Energy Storage Applications in Renewable Energy Systems Fabiano Salvadori, Oswaldo Hideo Ando Junior, Maurício de Campos, Paulo Sérgio Sausen, Eder Andrade da Silva, André Quites Ordovás Santos, and Fernando Marcos de Oliveira

Abstract Electricity remains a key element for world development, and the increase in the demand for electrical energy in the industrial, commercial and residential sectors, the predicted exhaustion of fossil fuel reserves (e.g. oil, coal), the environmental risks of nuclear energy, the effects of global warming in addition to other environmental issues makes it necessary to develop alternative/renewable and non-conventional sources for the electrical energy generation. Electricity generation, based on renewable non-conventional sources, can play an important role in global energy security and contribute to the reduction of greenhouse gas emissions. The use of these renewable energies can help to reduce energy consumption based on fossil fuel, which is the biggest source of CO2 emissions. Some of these alternative sources, e.g. wind, and photovoltaic, suffer from seasonality and intermittency, which represents a challenge to guarantee the adequate quality and dispatchability of the source. This limitation can be reduced and/or eliminated with the use of an Energy Storage System (ESS), allowing the energy system to be managed optimally.

F. Salvadori (B) LREI - Smart Grid Laboratory, Federal University of Paraiba - UFPB, João Pessoa, PB, Brazil e-mail: [email protected] O. H. Ando Junior GPEnSE - Energy and Energy Sustainability Research Group, Rural Federal University of Pernambuco - UFRPE, Cabo de Santo Agostinho, PE, Brazil e-mail: [email protected] M. de Campos · P. S. Sausen GAIC - Industrial Automation and Control Group, Regional University of the Northwest of the Rio Grande do Sul State - UNIJUÍ, Ijuí, Brazil e-mail: [email protected] P. S. Sausen e-mail: [email protected] E. Andrade da Silva · A. Q. O. Santos · F. M. de Oliveira GPEnSE - Energy and Energy Sustainability Research Group, Federal University of Latin American Integration - UNILA, Foz do Iguaçu, PR, Brazil © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_3

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1 Introduction The population growth observed worldwide plus the increasing levels of urbanization lead to a rapid growth in energy consumption and cause environmental concerns due to CO2 emissions. In addition, this urban population growth causes a mismatch between energy supply and demand [1, 2]. The solution to these problems requires, in addition to optimizing the use of electricity (rationalization), the minimization of toxic emissions caused by the use of fossil fuels (non-renewable) [3, 4]. In order to face the use of fossil fuels through the development of sustainable energy systems, several types of research have been developed to obtain electricity generation defined as clean [5]. The search for the solution to the environmental/energetic problem, in recent years, reflected in the rapid development of renewable energies, from non-conventional sources (e.g. wind and photovoltaic) [6]. Expanding employment opportunities, creating wealth, and improving people’s living conditions and the health of the planet, are possibilities envisioned with the decarbonization of the economy. The introduction of ESS technologies in electrical power systems made it possible to adapt the intermittence of renewable generation to the demand. There are several advantages related to the use of ESS in electrical power systems, in addition to storing and smoothing renewable energy. ESS can increase the power supply, resilience and efficiency of the system by means of many enforcement including: transmission/distribution congestion relief; load following; supply capacity; voltage support; energy timeshift; fast regulation; transmission/distribution upgrade deferral; area regulation; supply spinning reserve; renewable energy timeshift; power quality; renewable capacity firming; renewable energy smoothing; service reliability and black-start [7]. The rapid growth in demand for electrical energy and, consequently, in the electrical power system created new opportunities for the use of ESS. It should be noted, however, that many resources used in ESS solutions are not commercially or technologically mature, which greatly hinders their wide use. We can highlight that the ESS technologies are different in terms of implementation cost and technical characteristics such as: technical maturity; volumetric and gravimetric energy density; environmental impact; lifetime (years and cycles); volumetric and gravimetric power density; energy and power; discharge time; time response; operating temperature; self-discharge rate; requirement in terms of physical space for implementation; recharge time; memory effect (batteries); operation and maintenance (O&M) costs; recyclability; transportability and cumulative energy demand [7, 8]. Anomalies in an electrical power system can be significantly reduced through proper selection and management of energy storage systems and energy resources [3]. The main anomalies that occurred are: (a) lack of energy—occurrence of a total loss of energy supplied by the utilities; (b) power outage—short-term low voltage; (c) frequency variation—alteration in the supply frequency (50 or 60 Hz) causing instability in the system; (d) transients in the switching process—instantaneous under-voltage (nanoseconds); (e) over-voltage—a prolonged period of increased line voltage from

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a few minutes to a few days; (f) power spike (peak)—a short-term high voltage above 110% of normal voltage; (g) brownout—line voltage reduced for an extended period of a few minutes to a few days; (h) electrical network noise—a waveform caused by electromagnetic interference (EMI) or radio frequency interference (RFI); and, (i) harmonic distortion—generally caused by non-linear [9] loads. We must understand the Smart Grids, as a concept, or several concepts, than specifically a technology. They are electrical networks that intelligently and dynamically integrate the actions of all users/devices connected to them, from those that generate energy (suppliers), to those that consume it (consumers) and also those that develop both activities (prosumers) in order to provide electricity efficiently, sustainably, economically and safely. It is an electrical network that makes use of Information and Communication Technologies (ICTs), instrumentation and automation of the electrical network, and advanced systems of intelligence and systems management. In this way, it contributes to a convergence of technologies and supports the operations and systems that help companies manage their networks in the new reality where there is an injection of distributed energy resources, intermittent or not, and dispatchable or not. It can be highlighted that the differential of Smart Grids is the fact that they incorporate the digital transformation that changed the paradigm of uni-directionality, existing in electrical networks in the past, to the new paradigm of bi-directionality of energy and information.

2 Energy Storage Technologies Electric power systems operate within a logic based on a balance between supply (generation) and demand (consumption). Energy storage helps balance fluctuations in electricity supply and demand. This is done by storing energy during relatively high production and low demand and then releasing this energy into the electrical system during periods of high demand and low production. Energy storage can provide a number of benefits, for example, ancillary services, economic, reliability, and environmental (ecological) benefits. Depending on the complexity and size of the system being deployed, energy storage can help the electrical grid operate more efficiently, reducing the likelihood of power outages during peak demand periods and allowing more renewable resources to be installed [10]. Energy storage provides indirect environmental benefits, for example, energy storage can be used to integrate more renewable energy sources into the electrical system. It can also help to generate units operating at optimal levels and reduce the use of less efficient generating units that would otherwise only run at peak times. In addition, the additional capacity provided by energy storage can delay or even prevent the need to build new plants or expand transmission and distribution infrastructure [11, 12]. However, energy storage has some potentially negative impacts that will fundamentally depend on the type and efficiency of the storage technology. For example,

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Fig. 1 Ragone chart comparing specific energy and specific power of various energy storage technologies

batteries use raw materials like lithium and lead that can pose serious environmental hazards if not properly disposed of or recycled. In addition, electrochemical energy conversion processes have low performance. There are many ways of storing energy, each with its strengths and weaknesses. Some of the storage technologies are presented below and the Ragone chart comparing specific energy and specific power of various energy storage technologies is presented in Fig. 1 [13].

2.1 Pumped Storage Hydroelectric (PSH) Pumped storage hydroelectric plants (PSH) are those that have a mechanism for pumping water from a lower reservoir to an upper one according to the hydraulic flow. Like normal hydropower plants, they are also based on storing the gravitational energy of water across an elevation difference. The difference is that they have an accumulation system at a lower level that can be mechanically pumped to an upper level, through a duct by a reversible pump turbine using extra energy from another generating source. The water is pumped to a reservoir, using motor/pump sets, when it is released flowing through a turbine to generate electrical energy [14]. The biggest obstacle to hydraulic generation is the need for a water supply (rivers) to maintain the level of the reservoirs in conditions to operate the plant. Each generator has a minimum amount of water flow that makes the coupled turbine rotate. When this does not exist, the technology in a solution: pumping water from the lower reservoir

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Fig. 2 Basic components of the an reversible hydroelectric

to the upper reservoir. This can occur in a river with a weaker flow or even in an independent closed circuit of rivers, for a smaller plant. In both cases, the way to form the reservoirs depends on the topographic conditions, that is, taking advantage of the geography and the relative altitude of the areas necessary to build the two reservoirs. The simplest solution to implement a PSH project is the installation of a generating unit (turbine + generator) separate from another for pumping (motor + pump), selecting the most suitable configuration for both. However, the need for different equipment makes this arrangement more expensive and there are few occasions when it is adopted. For drops smaller than 600 m, reversible turbines are generally used, which operate both as turbines and pumps. Installing just one hydraulic machine means an economic advantage. Figure 2 shows a reversible hydroelectric plant and its basic components. Due to the way in which the reservoirs are constructed, many of the potential environmental impacts generated by a PSH are similar to those of a conventional hydroelectric plant. These are changing the flow, widening the river bed, raising the water table, changing climatic parameters, impacts on flora and fauna, social and economic impacts, etc. As most of these impacts are on a local scale, it is possible to carry out mitigating or compensatory actions to reduce the negative impacts of these projects, which are usually outweighed by the positive impacts generated.

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2.2 Compressed Air Energy storage in form of compressed air energy storage (CAES) is appropriate for both, renewable and non-renewable energy sources. The excess electricity, in this system, when in low electricity demand, is used to generate compressed air, and after, the compressed air, through expansion could run a turbine to generate electricity during periods with high energy demand [15, 16]. Energy can be stored in the form of compressed air, but the processes by which air can be compressed and expanded are diverse and depend on different parameters, especially state variables (pressure, volume and temperature—PVT), thus resulting in different final conditions. The various combinations of compressed air energy storage systems, according to the different phases of the system, are represented in Fig. 3. CAES systems can be classified in: (1) Adiabatic; (2) Diabatic; (3) Advanced adiabatic system and (4) Isothermal system [17]. 1. The adiabatic system (A-CAES)—in this process the heat of compression is used, which is stored at higher temperatures and, consequently, a greater amount of output power can be obtained. The air is heated during compression and stored in the tank. However, when the air temperature is minimal, more air mass can be stored. In this process, a large volume storage tank is needed to accommodate more air mass at higher temperatures. 2. An energy storage system based on air compression and storage in underground geological caves is defined as diabatic (D-CAES). The air is heated by combustion using natural gas or fuel and expanded in a turbine to generate electricity. This energy is stored and released during peak demand hours. In this system, the heat of compression is not used and is dissipated as waste.

Fig. 3 Combinations of CAES systems (adapted from [15])

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Fig. 4 CAES system configurations

Fig. 5 CAES system configurations

3. In AA-CAES systems, thermal storage is performed using a solid or fluid to store the heat from compression for later use during the expansion process. By using thermal storage, fossil fuel savings occur to reheat the air before expansion in the gas turbine. Currently, AA-CAES systems have shown potential for greater efficiency and lower greenhouse gas emissions. 4. The isothermal storage configuration (I-CAES) occurs when the temperature inside the storage tank is kept constant, removing heat during charging and providing this heat during the discharging process. In the Figs. 4 and 5 are presented schematically with all the processes discussed.

2.3 Flywheel For large-scale storage systems that represent a low environmental impact compared to other technologies, mechanical systems represent an attractive alternative. Among the different technologies for mechanical storage, the Flywheel Energy Storage System (FESS) is considered suitable for commercial applications (see Fig. 6).

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Fig. 6 A typical flywheel energy storage system

This system consists of a rotating mass, either composite or steel, inserted into a container with very low ambient pressure. The reduced pressure inside the vessel reduces the drag on the rotating mass, thus maintaining momentum and generating electrical energy for a longer period of time. The energy in a rotating mass (flywheel) is stored, and the kinetic energy produced is stored (dependent on the inertia and speed of the rotating mass) as rotational energy. The flywheel is placed inside a vacuum containment thus reducing energy losses due to friction, in addition, it is suspended by bearings so that the operation is stable. This keeps the flywheel turning without any additional power and with very little power loss [18, 19]. Purely mechanical flywheels were reported several hundred years ago to keep machines turning smoothly between cycles. Later, in 1950, one of the first energy production systems using a flywheel was produced in Switzerland, with a 1500 kg flywheel which they called “Gyrobus” [20]. However, more modern flywheels began to be developed in the 1970s, when NASA sponsored programs that proposed energy storage in flywheels as primary sources for space missions [21]. Current high-speed flywheel energy storage systems are constructed with a huge rotating cylinder supported on a stator, consisting of the stationary part of an electrical generator, by magnetically levitated bearings. For maximum efficiency, flywheel systems are operated in a vacuum to reduce friction between parts. The flywheel is connected to a motor-generator system that is connected to the electrical grid through power converters. The energy density of the most advanced FESS is quite significant/attractive with high efficiency and low losses. Four main features can be highlighted: (1) the rotating mass is made of fiberglass resins or polymeric materials with high mechanical strength; (2) this mass operates in a vacuum to minimize aerodynamic drag (friction); (3) the mass of the FESS operates at high speed; and, (4) To operate at high rotational speed use magnetic or pneumatic suppression bearing technology. Advanced FESS

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operates at a rotation frequency of over 100,000 RPM with top speeds in excess of 1,000 m/s. FESS is mainly used for high-power and low-power applications that require many cycles. An electrical machine transfers kinetic energy in and out of the flywheel. There are two modes of operation of this electric machine: motor or generator, which modes depend on the load angle. When acting as a motor, electrical energy is supplied to the stator winding, producing a rotating magnetic field which, interacting with the rotor magnetic field, produces torque on the rotor shaft. This will cause the rotor to spin rapidly and gain kinetic energy that will be stored by the flywheel. In its principle of acting as a generator, when it is then necessary to make use of the stored energy. The kinetic energy stored in the rotor applies torque, which is then converted into electrical energy. This causes the wheel to decelerate [18, 22]. A distinction must be made between the applications in which a flywheel is used to maintain an even angular speed of a machine and those in which it operates as a real energy accumulator, though the distinction is not always clear-cut. All modern flywheel accumulators include several elements such as the flywheel, a casing that is usually provided of a burst-containment structure and is able to maintain the rotor in a low-pressure environment, bearing and seal systems, power transmission, and usually vacuum and control systems. The accumulator must be studied as a whole and its optimization must be performed in the knowledge that the optimization of the components does not necessarily lead to an optimum whole. For example, an optimized flywheel might require a heavy or costly containment structure or power transmission, thus reducing the “efficiency” of the whole. One parameter commonly used to express the quality of an energy storage device is energy density, i.e. the ratio between the energy stored and the mass. It should be noted that the mass of the flywheel considered must be that of the complete system and the stored energy must be evaluated only as the energy that can be effectively supplied in normal operation. In flywheel development work, the energy density is often misleadingly presented by dividing the energy stored at burst speed by the mass of the flywheel alone. This practice is only justified by the fact that it is useful during the flywheel development work as it can be easily calculated from theoretical considerations or measured during a spin test and characterizes the flywheel itself. The flywheel design and material characteristics will define the rotor energy density, through Eq. 1:   σu en (1) = Kr , m a u. f. ρ where: en —energy (base of natural logarithms); m a —mass (mass of the molecules); K r —term expressing the dependence of the rolling resistance from the speed (Boltzmann constant); σu —ultimate strength of the material and ρ—density of the material. The so-called “shape factor” K depends only on the geometrical configuration of the flywheel and on the failure criterion used, at least under certain conditions. The

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ultimate strength of the material σu is easily defined in the case of isotropic materials and homogeneous flywheels. If the material is not isotropic, σu is a reference value and the failure criterion must be expressed accordingly. If the rotor is built using different materials, the values of σu and the density ρ are conventional ones, and the shape factor must be defined keeping into account also the properties of the materials. In order to overcome the above-mentioned limitations of the usefulness of the concept of energy density, an overall operating energy density can be defined as: 

en ma





  

=λλ λ o.o.

em ma



= λ λ λ K r

u. f.

σu ρ

(2)

where: the three coefficients λ (‘safety factor’—the ratio between the energy stored at operating speed and the one stored in burst conditions); λ (‘depth of discharge factor’—the ratio between the usable and the total energy stored, determined by Eq. 3) and λ (ratio between the mass of the flywheel and the mass of the whole accumulator), all smaller than unity, can be defined as follows: e=

1 2 Jω 2

(3)

The available (useful) energy of a flywheel can be determined within a range of speeds, minimum (ωmin ) and maximum (ωmax ):  2   1 ωmin 1  2 2 2 (4) e0 = J f ωmax − ωmin = Je ωmax 1 − 2 2 2 ωmax The value of λ is therefore:   ω2 λ = 1 − 2min ωmax

(5)

The value of λ is mainly determined by the type of transmission used. In most cases it is useless to lower the value of ωmin under a certain point: the advantages of using very low values of ωmin are small and the technological problems involved are often quite serious. In many cases ωmin is chosen as one half of ωmin : three-quarters of the total energy stored can thus be extracted from the flywheel. A “volume energy density” can also be defined as the ratio between the energy stored and the volume of the accumulator. If only the volume of the flywheel is taken into account, the volume energy density at burst speed can be calculated as: e V

u. f.

=K

σu ρ

(6)

Again Eq. 6 is valid for homogeneous rotors. If different materials are used in a flywheel, the shape factor K must refer to the average density, and Eq. 6 can be

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used. In order to compare kinetic accumulators with other energy storage devices, an overall operating volume energy density can be calculated in accord with Eq. 7: e σu (7) = λ λ λv K V o.o. ρ The coefficient λv is the ratio between the volume of the flywheel and the volume of the whole accumulator. ‘Volume of the flywheel’ is the actual volume occupied by the material of the flywheel and not the volume swept during rotation [23].

2.4 Supercapacitor Electrochemical capacitors (ECs) are known by names such as ultracapacitors, electric double-layer capacitors, or supercapacitors. This name difference is due to the fact that manufacturers use different names. Nippon Electric Company (NEC)TM defined supercapacitors as the first commercial device. On the other hand, the Pinnacle Research Institute (PRI) named ECs the ultracapacitor [24]. The ECs-based energy storage technology is unique in that it stores the flow of electric current by generating a magnetic field in which energy is stored. ECs store electrical energy directly in a magnetic field with very low losses due to superconducting coils (typically ≥ 97%). This technology is extremely efficient, fast, and flexible, however, having as a negative point the fact that it is expensive [25]. The superconducting material that conducts DC current has no resistive losses. A magnetic field is induced by the circulation of electric current in the coil in which the energy is stored. Electric current circulates in the coil indefinitely until it needs to be discharged. The superconducting property of the coil causes it to be supercooled at low temperatures, some in the range of 50–77 K and others like niobium-titanium alloys around 4.2 K [25, 26]. The amount of energy stored by these devices is related to the size of the coil and its geometry determines the inductance, L. The coil is an inductor and stores energy based on the square of the current (Eq. 8). E=

1 2 LI , 2

(8)

The amount of current flowing in the coil can be incredibly large. With a magnetic flux density of five Teslas, superconductors can carry currents of up to 300,000 A/cm2 this makes a cryogenic cooling system using liquid nitrogen or helium necessary, and this system has a loss of parasitic energy [27]. Capacitors store electrical energy by removing charge carriers (electrons) from one metal plate and depositing these carriers on another plate. Such charge separation creates a potential difference between the two plates, which can be harnessed by connecting a load to an external circuit.

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Considering a simple capacitor, the stored energy is given by Eq. 9 E=

1 V2 εA , 2 d

(9)

where: A is an area of parallel plates separated by a distance d; ε is the permittivity of the material (or medium) between the plates, and V is the applied potential difference. The capacitance C of the capacitor can be obtained using Eq. 10 C =ε

A , d

(10)

Using Eqs. 9 and 10, the energy stored in the capacitor as a function of capacitance can be obtained by Eq. 11: E=

1 C V 2, 2

(11)

To measure the capacitance (C) of the equivalent circuit, we can use another method that involves determining the change in energy that occurs during charging or discharging, which is given by Eq. 12:     1 (12) E = C V12 − V22 , 2 By integrating the instantaneous power we determine the energy change and therefore C can be calculated from Eq. 13 [24]:   2  t 2 t1 vidt  , C=  2 (13) V1 − V22 Then, analyzing Eqs. 11 and 13 it’s observed that the energy stored in a capacitor is directly proportional to the applied potential difference and its capacitance, consequently, the permittivity of the material between the capacitor plates. Furthermore, it is observed that the energy is also inversely proportional to the distance between the plates, therefore, the smaller the thickness of the material between the plates, the greater the energy stored. Traditional capacitors are generally not considered for large-scale energy storage due to the little energy they can store, but they serve to explain the concept of supercapacitors. Supercapacitors use the characteristics deduced earlier to increase their storage capacity, creating thin layers of charge storage. In supercapacitors, also known as double-layer capacitors, the electrical charge is stored in an electrochemical double-layer located on the outside of the electrodes, and not on the inside, as in batteries. Such a mechanism causes the differences between supercapacitors in relation to batteries, such as the short charging or discharging time and a longer useful life [24, 28, 29].

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Fig. 7 Comparative with different storage technologies

The electrolyte can be solid or liquid, and in the case of liquid electrolytes, a porous separator is positioned between the electrodes, isolating the parts, but allowing the passage of ions. Despite the possibility of a solid electrolyte, it usually consists of an organic liquid or an aqueous solvent, which contains dissolved ions that migrate from one electrode to another during charging or discharging. The cell voltage is approximately six volts, for higher voltage levels several cells need to be connected in series. For large systems this can result in a serious design problem as the typical failure mode of a cell is an open circuit. Therefore, if a single device fails, the entire system will be shut down, representing a high-reliability risk, a fact that must be taken into account when sizing the system. Due to the potential risk of operating at a higher voltage than the rated voltage in a cell, unlike batteries, supercapacitors cannot handle gases or dry out the electrolyte. To ensure safe operation and keep voltages within operating limitations, resistors or Zener diodes can be connected in parallel. Another alternative is that either each device can be loaded or unloaded individually [26]. Figure 7 shows the comparison of different storage technologies comparatively with supercapacitors.

2.5 Thermal Energy Storage (TES) Thermal energy storage (TES) is known as a technology that stores thermal energy by heating or cooling a physical storage medium, enabling the stored energy to later be used in electrical power generation and heating and cooling applications [4]. Some heat sources: are natural gas; solar thermal energy; propane (LP); oil; nuclear centers;

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coal; wood; electricity; heat pumps; geothermal energy; fossil fuel plants; industrial waste heat and biomass. The advantages of using TES in a power system: (i) largest economy; (ii) reductions in investment and O&M costs; (i) increase in efficiency; (iii) improved reliability and (iv) less pollution in the environment (lower CO2 emissions) [30, 31]. Technologies for generating electricity that uses solar thermal systems are industrially developed and use most of the thermal energy of the Sun during the day. However, it has no (thermal) backup to maintain operation at night or in hours of low or no solar radiation. These systems are becoming important for energy storage in hybrid systems, in combination with concentrated solar power plants, where solar heat is stored for electricity production when sunlight is not available. The development of new materials, characterized and improved in their thermophysical properties, will enable 24-hour operation in an efficient TES system. In accord with [30], by the more extensive use of heat and cold storage, it is estimated that in Europe, around 1.4 million GWh/year and 400 million tons of avoided CO2 emissions can be saved, in buildings and in industrial sectors. The use of thermal storage, despite not being able to be used as an effective backup, helped the system to thermally stabilize, consequently, thermal storage found use in hybrid thermal/solar systems. As a result, studies focused on the development of thermal energy storage technologies have increased, as well as the applicability and their effects on sensible and latent heat storage in numerous applications [32, 33]. Thermal energy generation, according to the convenience from the burning of fuels, however, if it is not consumed, it is simply dissipated in the environment resulting in waste. Therefore, if there is an increase in demand, there will be a need for a greater generation and consequently burning of fuels to meet this demand. This has the following implications [33]: • There is an increase in the emission of harmful gases (e.g. CO2 ) into the atmosphere due to the burning of fuel causing a greenhouse effect and also the release of unused heat into the environment (greater global warming). • The increase in the consumption of fossil fuel, non-renewable, causes an increase in the cost. In addition, there is the character of waste, as the thermal energy available for free from a renewable source such as solar radiation is no longer used. The classification of thermal energy storage technologies is presented in Fig. 8 based on the criterion of the state of the energy storage material.

2.5.1

Sensible Heat Storage—SHS

In the sensible heat storage (SHS) method, thermal energy is stored by heating or cooling a liquid or solid storage medium (e.g. water, sand, molten salt or rocks). This stored thermal energy can be determined by Eq. 14 [33, 34].

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Fig. 8 Classification of thermal energy storage technology

Q=

T2

  (m) c p dT

(14)

T1

where: m is the mass (kg); c p is the specific heat capacity (kJ/kg (K) and T1 e T2 is the temperature range. During the thermal energy absorption process, there are no phase changes in the material, while the material’s temperature increases. The amount of stored heat is proportional to the density, the volume, the specific heat, and the temperature change [30]. The most common commercial heat-storing medium is water, which has both residential and industrial applications. Underground storage using liquid and solid mediums is also used, typically in large-scale applications. Nevertheless, the storage capacity of such systems is restricted by the medium’s specific heat. SHSs systems have a typical capacity between 10 and 50 kWh/t and storage efficiencies between 50–90%, depending on the specific heat and the thermal insulation technology. The cost of a complete SHS system is usually in the range of e 0.1–10/kWh, depending on the size, application, and thermal insulation technology employed. Some lower-cost solid-state materials exist, but their lower heat capacities often require the system to be unrealistically large [30, 35]. The water medium operates in a temperature range from 25–90 ◦ C [33]. Its advantages are its high specific heat, non-toxicity, low cost, and high availability. Water has also a few disadvantages, like high steam pressure, and corrosivity. The tanks may be built using steel, aluminum, reinforced concrete, and fiberglass. Glass wool, mineral wool, or polyurethane may be used as insulators. Their sizes can vary from a few hundred liters to thousands of cubic meters. To preserve the thermal performance, the use of large reservoirs is suitable. However, this type of tank requires the development of technologies to guarantee watertightness, which makes it possible to

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minimize heat losses caused by the diffusion of steam through the walls and optimize the stratification inside the tank, [33]. Mineral oil is primarily used as a Heat Transfer Fluid (HTF) in high-temperature solar plants. It collects the heat in the receiver and then transports it to the boiler, where the steam is generated. It can also be used to store thermal energy at night in highly insulated storage tanks. When HTF is used as an energy storage material, it is a straightforward system that eliminates the need for a heat exchanger, reducing cost. Mineral oil has a lower steam pressure than water and can operate at higher temperatures up to 400 ◦ C. Moreover, unlike molten salts, mineral oil does not freeze overnight. But mineral oil is expensive compared to molten salts, and recently low melting point molten salt mixtures have been found to replace other alternatives [33].

2.5.2

Latent Heat Storage—LHS

This technology stores latent heat in materials that can release or absorb energy with a change in the physical state of materials. They are commonly known as Phase Change Materials (PCMs). PCMs have greater storage capacity due to the greater energy storage density. The heat is mainly stored in the phase-change process, at a quite constant temperature, and it is directly related to the latent heat of the substance. They are classified into organic and inorganic. Organic PCM is classified as paraffin and non-paraffin. Paraffin wax represents a good example. Non-paraffinic PCM includes fatty acids, esters, and glycols. Inorganic PCM includes salt hydrates, salts, metals, and alloys. Examples are sodium sulfate decahydrate, calcium chloride hexahydrate, sodium thiosulfate, and the like [30]. PCMs can increase storage capacity with efficiencies between 75–90%. In most cases, storage is based on a solid/liquid phase change with energy densities on the order of 100 kWh/m3 . In these systems, the main costs are associated with the heat (and mass) transfer technology, which must be installed to achieve sufficient charge/discharge power. The costs of latent heat storage systems range from 10 to 50 e/kWh [34, 35].

2.5.3

Chemical Thermal Storage—TCS

Chemical thermal storage (TCS) can provide greater storage capacities. Thermal reactions (e.g., adsorption or adhesion of a substance to the surface of another solid or liquid) can be used to accumulate and discharge heat involving a reversible reaction in which heat is stored during the endothermic reaction step and released during the exothermic. During charging, a chemical reactant is dissociated into products in an endothermic reaction using thermal energy. The generated products can be easily separated and stored until a discharge is required. During the discharge step, the stored products are mixed at a suitable pressure and temperature, starting an exothermic reaction, and releasing energy.

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Despite its attractive application for long-term energy storage, TCS technology is still in the early stages of laboratory or pilot plant research [36]. Examples of some materials currently investigated are: metal hydrides (MgH2 and CaH2 ), carbonates (PbCO3 and CaCO3 ), hydroxides (Mg(OH)2 ) and (Ca(OH)2 ), oxides (BaO2 and Co3 O4 ), ammonia system (NH4HSO4 and NH3) and organic systems (CH4 /H2 O, CH4 /CO2 , C6 H12 ) [37]. TCS systems can achieve storage capacities of up to 250 kWh/t with operating temperatures in excess of 300 ◦ C and efficiencies from 75% to nearly 100%. Its storage and transport period are theoretically unlimited because there is no thermal loss during storage, as the products can be stored at room temperature. The associated costs are estimated to be between 8 and 100 e/kWh [35, 36].

2.6 Battery Batteries are the result of the junction of several electrochemical cells and can be classified as secondary or primary. The secondary ones are configured as rechargeable batteries, being this one of the EES that present greater performance in the industry and in everyday life. In Fig, the simplified process of the operating principle of a typical BESS system is shown. A BESS system consists of a number of batteries connected in series or parallel, which produce electricity at the desired voltage, doing so from a chemical reaction. In this segment, a cell can bidirectionally convert the available energy between electrical and chemical energy. During discharge, electrochemical reactions occur at the anodes and cathodes simultaneously. For the external circuit, electrons are supplied from the anodes and are collected at the cathodes. During charging, reverse reactions take place and the battery is recharged by applying an external voltage to the two electrodes [38]. Electrochemical storage devices are available in different size ratios, which corresponds to one of the main advantages of these technologies. Thus, electrochemical storage systems (EcSS) can be composed of several technologies [3] (Fig. 9).

2.6.1

Lead Acid Batteries

Lead-acid batteries are considered one of the oldest technologies in terms of rechargeable batteries. In this type of battery, the cathode is made of lead dioxide and metallic lead anode, which are immersed in a dilute sulfuric acid electrolyte. The main advantages of using lead-acid batteries are high energy efficiency, low self-discharge rates, relatively high cycle efficiencies, and low initial capital cost. However, it suffers from some disadvantages which include relatively low cycle times (up to 2000), low lifetime, low energy density, and low specific energy ratio. Furthermore, systems composed of this technology may require a thermal management system, as leadacid batteries may perform less than expected when exposed to low temperatures, a fact that may increase the total cost of an ESS [39, 40].

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Fig. 9 Simplified schematic of the operation of a BESS

Lead-acid batteries are suitable for systems that require short or medium storage times and especially in cases where cost-effectiveness, reliability, and tolerance for misuse are considered critical, but factors such as energy density and service life are considered important. The biggest markets for this technology are the automotive industry and telecommunications back-up UPS systems [39–41].

2.6.2

Lithium-Ion (Li-Ion) Batteries

It is presented as an advanced rechargeable battery technology, commercially developed by SonyTM , during the early 90’s. Lithium-ion batteries are so named because they do not use metallic lithium as an electrode, instead using lithium ions. lithium, present in the electrolyte in the form of lithium salts dissolved in non-aqueous solvents. During the discharge process, lithium ions migrate from the anode material to the cathode material and electrons move along the wire of the external circuit (electronic conduction). Li-ion batteries have high efficiency, high energy density, a good response time (in milliseconds), and a low self-discharge rate. However, the disadvantages of the Liion battery are depth of discharge (DoD) cycle, sensitivity to high temperatures, and high cost. However, the cost of lithium-ion cells tends to decrease with large-scale production [40, 42]. Due to their operational characteristics, lithium-ion batteries are the main energy storage devices for portable electronics, such as smartphones, notebooks, TVs, and tablets. In addition, these characteristics also make these batteries suitable for electric vehicle traction applications, electric tools, and intermittently available renewable energy storage [42]. Li-ion batteries typically have four main chemical elements used in cathode materials: manganese (Mn), cobalt (Co), nickel-cobalt-manganese (Ni–Co–Mn), and phosphate (PO3− 4 ) using carbon (C) or graphite as the anode. Among these, the one

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with the highest energy density (Wh/kg) is cobalt oxide (CoO). It so happens that CoO has as a negative characteristic the fact that it is thermally unstable since its internal resistance varies considerably with time and depends on energy production, resulting in a reduced life cycle. On the other hand, manganese oxide (MnO2 ) has positive characteristics in its low cost, high energy density, and safety. However, it has limitations in terms of limited operating temperature and low volume energy density [43].

2.6.3

Sodium Sulfur (NaS) Batteries

These types of batteries are composed of molten electrodes (sodium and sulfur) and beta-aluminum electrolyte in a non-aqueous state. Sodium is used as a negative electrode and sulfur is treated as a positive electrode. In this type of technology, a temperature of 300 ◦ C to 350 ◦ C (574 to 624 K) is required for reactions to occur, thus ensuring that the electrodes are in a liquid state. The advantages of this battery are the high energy density, low rate of daily self-discharge, and high nominal capacity when compared to other technologies used in batteries. However, they present limitations due to the high operational cost, due to, among other factors, the need for a heating system that maintains its operating temperature [3, 38]. NaS batteries find application in the operation of the electrical distribution network, integration of intermittent generation (wind and solar), and ancillary services. NaS technology has the potential for application in ancillary services due to its long discharge time (up to 6 h) and low self-discharge. These batteries are able to provide quick response, adequate to the needs of the electrical system, such as the mitigation of power quality events. Furthermore, the NaS battery is considered one of the most promising for high-power ESS applications [3].

2.6.4

Nickel Cadmium (NiCd) Batteries

The nickel-cadmium (Ni–Cd) battery, due to its electrochemical characteristics, make it particularly suitable for applications subject to extreme environmental factors, e.g. ambient temperature, need to be taken into account and where service life, charge/discharge characteristics, cycling behavior, maintenance requirements, and life-cycle cost are important parameters [44]. Chronologically, NiCd batteries were the second type of rechargeable battery developed. It was introduced by Swedish engineer Waldemar Jungner in 1899. NiCd batteries use nickel hydroxide and metallic cadmium for the two electrodes and an aqueous alkaline solution as the electrolyte. Normally NiCd batteries have high robustness and low maintenance costs. However, nickel and cadmium materials are toxic metals to the environment, a fact that results in environmental risks and batteries that use NiCd technology are also subject to the “memory effect”, that is, the maximum capacity of the battery can be drastically reduced if it is recharged repeatedly after being only partially discharged. For these reasons, NiCd batteries

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are increasingly in disuse and there is no quotation for this technology for large-scale ESS applications in the future [44, 45]. Energy storage systems have already incorporated several battery technologies. The choice between these different technologies depends on the technical and economic characteristics of the particular application to be implemented. The two dominant industrial battery technologies today are lead-acid and nickel-cadmium. In addition to these, there are other pairs such as vanadium redox, sodium-sulfur, zincbromine, and lithium-based types. As far as commercial (non-research) applications are concerned, the problem with less conventional technologies will likely be the cost of deploying cells in large volumes [38, 40, 44]. The long life, low maintenance, and frequent cycling characteristics of nickelcadmium batteries make them particularly suitable for applications where ambient temperatures are extreme.

2.6.5

Nickel–Metal Hydride (NiMH) Batteries

A NiMH battery consists of a positive electrode based on oxyhydroxide oxide, a negative electrode based on metallic cadmium, and an alkaline electrolyte (usually potassium hydroxide). It is an evolution of the nickel-cadmium (NiCd) battery, the NiMH battery has a higher power and energy density, has a lower environmental impact, as well as is less likely to suffer memory effects [9, 15]. NiMH batteries have a longer lifespan compared to Li-ion batteries. They have a wide variety of applications, ranging from portable products to electric cars, this technology also has potential for industrial applications. However, the significant barrier to application of this in EES is the high rate of self-discharge. NiMH batteries also have deep cycle sensitivity, so performance degrades after a few hundred full cycles [4].

2.7 Fuel Cells The beginning of fuel cell (FC) research took place over 100 years ago, but with the great development in the new materials area in the last 15 years, this technology, associated with the growing demand for low environmental impact, has become very promising in the world energy scenario [46]. The working principle of fuel cells is similar to that of batteries, but they do not run out or need to be recharged. From the supply of fuel, the fuel cells return electricity and heat as long as it is supplied. The basic structure of a fuel cell consists of a negative electrode (anode) and a positive electrode (cathode), compressed around an element (electrolyte). The anode is powered by a fuel, e.g. hydrogen, while air is injected into the cathode. In the case of a hydrogen-based fuel cell, the hydrogen molecules are split at the anode into protons and electrons by a catalyst, then following different paths to the cathode. Electrons circulate through an external circuit, creating a flow

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of electricity and protons migrate through the electrolyte to the cathode, combining with oxygen and electrons, thus producing water and heat [46, 47]. One of the main limitations of the development of fuel cell technologies, based on hydrogen, is the hydrogen storage process, which constitutes a key technology, in applications of electric mobility, portable energy storage, and stationary generation of electric energy. An important point to note in this case is that hydrogen has the highest energy/mass of any other fuel currently in use. It has, however, as a disadvantage, its low density at room temperature which results in a low energy/unit volume. Therefore, for fuel cells to represent an economically and technically viable alternative, it is essential to develop advanced technologies for hydrogen storage that have the potential for greater energy density. This conversion takes place by means of two partial chemical reactions at two electrodes separated by an appropriate electrolyte, that is, the oxidation of a fuel at the anode and the reduction of an oxidant at the cathode, with the aid of specially developed catalysts, indicated in the reactions (15), (16) and (17) [46]: Anode : H2 + 2H2 O ⇒ 2H2 O + 2e−

(15)

1 O2 + 2H3 O+ + 2e− ⇒ 3H2 O 2

(16)

1 Overall − r eaction : H2 + O2 ⇒ H2 O 2

(17)

cathode :

Choosing, for example, hydrogen as fuel and oxygen as oxidant, the so-called acid cell results in the formation of water and heat production, in addition to the release of free electrons, which can generate electrical work. A simplified schematic of an acidic FC is shown in Fig. 10. Unlike combustion engines, which have their maximum theoretical efficiency limited by the Carnot cycle, the efficiency theory of FC is given by the quotient between the energy free reaction G r and the enthalpy of reaction Hr in accord with Eq. 18 ηec =

G r Hr

(18)

The electrochemical efficiency (ηec ) given by Eq. 18 has a weak temperature dependence when compared to the efficiency given by the Carnot cycle. Thus, CC makes it possible to obtain high efficiencies, that is, a better use of fuel, even and especially at low temperatures. There are several types of FC, classified according to the type of electrolyte they use and, consequently, its operating temperature. Table 1 lists the types of CC developed to date with their main features, advantages and disadvantages current and their most relevant applications.

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Fig. 10 Simplified drawing of a fuel cell Table 1 Types of Fuel Cells (where: PEMFC—Proton Exchange Membran Fuel Cell, PAFC— Phosphoric Acid Fuel Cell, MCFC—Molten Carbonate Fuel Cell and SOFC—Solid Oxid Fuel Cell) ◦C Type Electrolyte Advantages/disadvantages  + O PEMF C Polymer H3 20–120 High power density Flexible operation Mobility High membrane and catalyst cost Contamination of the catalyst with CO   + PAFC 160–220 Greater technological development (H3 PO3 )/ H3 O CO tolerance Electrode porosity control Corrosion limited efficiency MCFC Fused carbonates 550–660 CO/CO2 tolerance   CO2− Ni-based electrodes 3 cathode corrosion Difficult to control three-phase interface SOFC Zr O2 850–1000 High efficiency (favorable kinetics) Fuel reform can be done in the cell (Zirconia) Material problems O2− Thermal expansion

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3 Renewable Energy Sources Coming from natural sources, which are replenished at a rate greater than their consumption, energy obtained from renewable sources has become attractive in recent years due to several factors, including environmental and economic factors. Sunlight and wind, for example, are sources that are constantly being replenished. An interesting feature of renewable energy sources is that they are abundant and all around us. Fossil fuels—coal, oil, and gas—on the other hand, are non-renewable resources that take hundreds of millions of years to form. In addition, burning fossil fuels to produce energy causes harmful emissions of greenhouse gases (CO2 ). Emissions from renewable energy generation are much lower than those obtained from burning fossil fuels. The transition from fossil fuels, which currently account for the majority of emissions, to renewable energy is one of the key points to tackling the climate crisis. The main aspect of a renewable energy source is its sustainability aspect, something that does not end, or that we can consider as infinite as the sun. The term alternative energy also refers to renewable energy sources, because these energy sources are alternatives to the most commonly used non-renewable sources such as oil, coal, etc. The use of renewable energy sources is, in most countries, cheaper and generates three times more jobs than fossil fuels.

3.1 Small Hydroelectric Plants—SHPs The (SHPs) are facilities that result in lower environmental impacts and lend themselves to distributed generation. Hydraulic energy comes from solar radiation and gravitational potential energy, through evaporation, condensation, and precipitation of water on the earth’s surface. Unlike other renewable sources, they already account for a significant portion of the world’s energy matrix and has duly consolidated technologies. A typical SHP normally operates on a run-of-river basis, that is, the reservoir does not allow the regularization of the water flow. Among the sources of renewable energy, this is the one that has the least impact on the environment and on the physical environment, it regulates the river, preventing flooding. Hydroelectric plants can be classified according to their size, ranging from large plants, supplying electricity to many consumers, and small and micro plants, which are operated by individuals for their own consumption of the electricity generated or to sell the energy to utilities. Although we have different definitions, the United States Department of Energy (US/DOE) defines: (a) small power plants (generating between 100 KW and 10 MW) and (b) micro-hydroelectric power plants (up to 100 KW). Hydroelectric power plants can be classified under three different categories [48]:

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• Impoundment hydroelectric: Most common type, large generation capacity, needs a big raised reservoir behind the dam. • Diversion hydroelectric: Manages the river flow (tide) to propel the turbine, does not need a reservoir, low generation capacity. • Pump storage hydroelectric: Similar to impoundment hydroelectric, pumps water back to the reservoir when demand is low for later use They can also be classified by their power generation capacity as [48]: • Micro: Generates less than 100 [kW]. • Small: From 100 kW to 30 [MW]. • Large: Greater than 30 [MW] Other alternative classifications are made about flow control, load conditions, and head altitude. The possibility and degree of flow control, which is available, varies from installations that store water in reservoirs (small or large) to run-of-river systems, that is, that do not retain much water and are thus subject to natural fluctuations. of the river flow and is subject to the rainfall regime [48]. For isolated communities, small towns, or industries, small hydroelectric plants (SHPs) represents one of the most important sources of energy, being able to provide energy reliably and uninterruptedly. We define a hydroelectric plant as an SHP when its installed capacity is less than 30 MW and it has a reservoir, considered small (less than 3 km2 of dedicated water surface area [49]). The possibilities of developing SHPs as a renewable source of energy are great all over the world. In addition to the main objective of generating electricity, the SHPs collaborate by regulating the flow of water, in water supply, preservation of the habitat of native species, and various recreational uses. Impoundment hydroelectric generators (Fig. 11) use the most basic operation principle, as they are based on converting the potential energy of raised accumulated water into a quick flow to move a generator’s propeller. There are six fundamental components in this type of power plant: dam, reservoir, penstock, turbine, generator, and governor.

3.2 Tidal Energy Of all the technologies currently available for generating electricity, tidal technology has the lowest theoretical potential, around 1,200 TWh/year. This is a consequence of its very location-specific nature, as only a few countries can really take advantage of this feature. A subcategory of tidal technology, namely tidal range, dominates the current cumulative global installed capacity for ocean energy technologies. For logistical, environmental, and technological reasons this theoretical potential cannot be fully implemented. Recently, the development of new technologies to take advantage of this potential has been presented and many of them are still being studied [50–52].

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Fig. 11 Impoundment hydroelectric power plant representation

From the movement of the tides in the oceans, the generation of electrical energy occurs. These power generation systems already involve advanced technologies that allow their use on a commercial scale. The technology used in this case is similar to that of conventional hydroelectric generation schemes. Two examples, already in operation, can be cited: • The Rance Tidal Power Station is a tidal power station located on the estuary of the River Rance in Brittany, France. • Sihwa Lake Tidal Power Station, operated by Korea Water Resources Corporation, is the world’s largest tidal power facility, with a total power production capacity of 254 MW. When completed, it surpassed the 240 MW of the Rance Tidal Power Station, which was the largest in the world for 45 years. The best mechanism for exploiting tidal energy is to employ estuarine barrages at suitable sites with high tidal ranges. The technology is relatively mature and the components are commercially available. However, the pace and extent of commercial exploitation of tidal energy will be significantly influenced by both the treatment of environmental costs of competing fossil fuels and by the availability of construction capital at modest real interest rates. The larger projects could require the involvement of national governments if they are to succeed [53].

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3.3 Geothermal Energy Geothermal energy is characterized by heat coming from the Earth, it is heat energy generated less than 64 km from the earth’s surface, in a layer of rocks (magma), which reaches up to 6,000 ◦ C. Electric energy can be obtained by drilling the soil in places where there is a large amount of steam and hot water, these must be drained to the earth’s surface through specific pipes. Then the steam is transported to a geothermal power plant, which will turn the blades of a turbine. Finally, the energy obtained by moving the blades (mechanical energy) is transformed into electrical energy through the generator. The positive aspects of this type of energy are: (i) the emission of polluting gases (CO2 and SO2 ) is practically nil, not intensifying the greenhouse effect, unlike fossil fuels; (ii) the area required for the installation of the plant is small and it can supply isolated communities. The negative aspects are considered: (i) it is a very expensive and unprofitable energy, as it requires high structural investments and its efficiency is low; (ii) it can cause the depletion of the geothermal field; (iii) the lost heat raises the ambient temperature and (iv) there is the emission of hydrogen sulfide (H2S), extremely corrosive and harmful to health. Compared to other renewable sources, geothermal heat is advantageous as it is available daily and in all seasons. This fact makes geothermal energy an attractive option for sustainable energy supply. Added to this idea, it corroborates the fact that geothermal energy is considered a clean and ecological form of energy, as it allows the generation and sale of electricity with low emission of harmful pollutants into the atmosphere [54]. The amount of thermal energy extracted from a geothermal system compared with the natural through-flow of energy is very important with regard to the sustainability of the resource. For convenience we introduce the term “production ratio” or PR defined by 19: PR =

PEF NEF

(19)

where: PEF is the produced energy flow and NEF is natural energy flow. A geothermal system, when in its natural state (pre-production), can be represented by the lumped parameters model shown in Fig. 12. In this case, the surface heat flux Q sur f is equal to the deep flux Q deep , that is: Q sur f = Q deep

(20)

Accurately estimating Q sur f is not trivial, as the manifestations occurring at the surface may be small and it may occur that part of the upward flow of heat, from greater depths, may be lost to shallow underground aquifers. The producing wells, during production, have their energy extracted at a rate Q pr od . Some additional convective flow and deep hot recharge, Q r ech , may occur

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Fig. 12 Diagrammatic sketch of the natural state of a geothermal system

from the pressure decline caused by the production. Thus, the rate of heat extraction from the system, Q extr , is given by: 

Q extr = Q pr od + Q sur f − Q deep − Q r ech

(21)

Note that in Eq. 21 the energy flow from the surface, Q, is not the surf same as in the natural state [55].

3.4 Biomass Energy The term biomass is used to name the group of energy products and renewable raw materials, originated from organic matter formed biologically. From an energy point of view, biomass is any renewable resource derived from organic matter (of animal or plant origin) that can be used for energy production [39]. The generation of energy through biomass can help to reduce dependence on fossil fuels and hydroelectric plants, diversifying the energy matrix without losing its renewable character. Therefore, new research, new investments, and economic feasibility studies of alternative sources of raw materials for energy generation are necessary. The use of biomass for the generation of electric energy is fully part of the concept of distributed generation since the generating units must be located close to the place where the biomass is produced, thus avoiding raising the costs of transporting this raw material and consequently the generation cost. The use of cultural residues can contribute to the decentralization of generation, reducing investments in transmission lines and energy losses due to the shortest distance. On the contrary from other energy sources, such as solar and wind, which are intermittent sources, biomass energy can be used at any time (dispatchability). Biomass can be classified into two large groups [39]: (i) traditional biomass, essentially composed of firewood and natural waste, and (ii) modern biomass, produced from advanced and efficient technological processes, such as liquid biofuels, briquettes and pellets, cogeneration (sugarcane bagasse) and dedicated crops of species such as planted forests and sugarcane.

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The main source for generating energy from biomass is waste, especially those of plant origin. Through biomass, it is possible to obtain different forms of energy. Its main uses as an energy input are: the production of solid biofuels for thermal energy generation (coal and agroforestry residues), liquid biofuels (fuel alcohol and biodiesel used in combustion engines), and electric energy generation (direct combustion, gasification, of gases, among other technologies). Biomass can be obtained from woody and non-woody vegetables and/or from organic residues and transformed into energy through different conversion processes [56, 57].

3.5 Photovoltaic Energy The global photovoltaic generating capacity has doubled in 3 years, considering from 2018, bringing the world solar generating capacity to a capacity of TW in April 2022. For the world solar capacity to reach 1 TW in 2022, from its 100 GW in 2012 took about a decade. It turns out that the global solar market is growing exponentially. According to Solar Power Europe, in just 3 years, global solar energy will more than double to 2.3 TW in 2025. Representing more than half of the 302 GW of renewable capacity installed internationally in 2021, solar energy remains the fastest growing renewable energy. With 168 GW of additions, solar installed over 70 GW more than the next largest installer—wind—and more than all non-solar renewables combined. With an annual growth rate of 14% and an all-time high of 54.9 GW of new solar power, China maintained its market lead in 2021, adding twice as much solar capacity as the second largest market, the United States. The United States, however, saw impressive growth, with 42% more additional solar energy in 2021 than in 2020. India regained its third position with 14.2 GW of solar installations. Solar cell technology has benefited from the high standard of silicon technology originally developed for transistors and later for integrated circuits and the quality and availability of high perfection monocrystalline silicon (Si). In the early years, only single crystals have grown in Czochralski (Cz) were used for solar cells and although this material still plays an important role, currently there are other single crystal options [58]. Currently, there are three main PV panel technologies: silicon panels, thin film panels, and hybrid panels. The first group is divided into two categories: monocrystalline silicon (mono-Si) and polycrystalline silicon (p-Si). The second group contains amorphous silicon (a-Si), cadmium telluride (CdTe), organic photovoltaic cells (OPV), and copper, indium, and gallium selenide (CIS/CIGS). And the last group is panels that generate energy and heat water simultaneously. Figure 13 shows the different material technologies used in PV panels. The silicon module is the first generation of photovoltaic technologies. Despite being the first technology, it is constantly improving its capability and efficiency. The main types of modules are monocrystalline, which constitute about 80% of the current market, and polycrystalline [59]. The main benefits of silicon modules are

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Fig. 13 PV panel materials technologies

performance, linked to the high efficiency of this technology, reliability, related to the modules’ durability of more than 25 years and little degradation in this period, and abundance, since silicon is the second most abundant in the earth’s crust [60]. CIGS (copper-indium-gallium-selenium) is one of the main materials currently used in the manufacture of thin-film photovoltaic cells. CIGS has a good sunlight absorption coefficient, allowing the use of thinner layers than would be required with other types of semiconductors. CIGS cells have already been manufactured in laboratories with efficiencies greater than 21% and commercially the modules have efficiencies ranging from approximately 15–18%, very close to the efficiencies of crystalline silicon modules. Furthermore, better uniformity is achieved with the use of selenide, decreasing the number of recombinations in the film, benefiting the quantum efficiency and therefore the [59] conversion efficiency. The main problem to be solved concerns making the process high yield and low cost. Various deposition methods are used: sputtering, “ink” printing, and electroplating with different yields and efficiencies [61]. The benefits of CIGS technology are not limited to the low cost, easy availability, and thermal expansion of soda-lime glass that is commonly used as a substrate. But also with the increase of the electrical effect which is an improvement in the conductivity of the module [62]. After choosing the technology for the photovoltaic modules, an important step is to predict the electrical behavior of the photovoltaic system, based on previous information specific to each location, such as the temperature and irradiation of the place. This step is important to obtain a forecast of how much energy will be delivered after installing the photovoltaic generator. A solution to this problem is the mathematical modeling of the system, which is usually based on the ideal model and the one diode model [63].

3.5.1

Simulation Models

The two electrical models, of photovoltaic panels, most used for simulations are the ideal model and that of one diode. One aspect of simulation that requires attention is the estimation of parameter values, which are the model’s components and variables.

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In general, the data provided by the manufacturers (datasheets), gives the open circuit voltage (VOC ), short circuit current (I SC ), current at the maximum point power (I M P P ), the voltage at the maximum power point (VM P P ) and power at the maximum power point (PM P P ). In some cases, the values of the temperature coefficients for open circuit voltage (K v ) and short circuit current (K i ) are also specified. In addition to this information, the models need other values that are not cataloged, such as the photovoltaic current (I P V ), reverse saturation current (I0 ), diode ideality factor (α) and the resistors in series (Rs ) and parallel (R p ). In addition to the functions normally available (maximum power points, short circuit current, and open circuit voltage) functions derived from these can also be used to obtain new equations. The simplest method is constant parameter modeling, where it is assumed that only I P V and I0 are affected by environmental variations, while the other parameters (α, Rs and R p ) are considered constant for a given condition. Although these parameters considered constant are sensitive to the variations of G and T , it is possible to find satisfactory values for the simulations of the models in this way [64]. • Ideal Model This is the simplest model, but the least accurate in terms of the characteristic at the maximum power point of the photovoltaic panel. This model can be understood from a simple analysis of Kirchhoff’s Laws, without the need to use any additional tools. From this analysis, we obtain (22) for the output current [65]. I = IPV − ID

(22)

where: I D is the diode current modeled from (23) (Shockley equation), which represents the diode model.     qV −1 (23) I D = I0 exp αkT where: q—electron charge (1.6 x 10-9 ); V —PV cell voltage; α—diode ideality factor; k–constant of Boltzmann (1.38064852 × 10−23 J/K), and T —junction pn temperature. Obtaining I0 from (24)  I0 = q An i 2

Dp Dn + L p Nd L n Na

 (24)

where: A—cell area; n i —concentration of intrinsic carriers of the material; D p , Dn —diffusion coefficient of holes and electrons in the material; L p , L n —diffusion length of holes and electrons, respectively; Nd , Na —concentration of n-type and p-type dopants.

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The ideal model is represented by (25) I = I pv

    qV −1 − Io exp αkT

(25)

• One Diode Model The one diode model, for most authors, is seen as the most faithful model of the photovoltaic cell. In it the power loss factors are portrayed as small resistors, Rs and R p . The parallel resistance (R p ) represents the leakage current, while the series resistance (Rs ) models the internal losses due to electrical connections and current flow, in addition to contributing to the model efficiency [66]. Using Kirchhoff’s Law it is possible to obtain the output current (26) I = IPV − ID − IP

(26)

the currents I P V , I D e I P are defined from (27), (28) and (29) G Gr     V + Rs I −1 I D = I0 exp αVT I P V = (I P V,r + K i T )

IP =

V + Rs I Rp

(27)

(28)

(29)

where: I P V,r —rated photovoltaic current; T —difference between current and nominal temperature; G—irradiance; G r —reference irradiance; R p —parallel resistance; Rs —series resistance; I P —current related to the electrical losses of the resistors (Rs and R p ). The reverse saturation current, of the diode, is obtained from (30) I SC,r + K i T    I0 =  +K v T exp VOC,rαV − 1 T

(30)

where: VT —thermal potential; I SC,r —rated short circuit current; VOC,r —rated open circuit voltage.

3.6 Wind Energy The global wind industry had its second-best year in 2021, with almost 94 GW of capacity added globally, trailing behind 2020s record growth by only 1.8%. Total global wind power capacity is now up to 837 GW, helping the world avoid over 1.2

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billion tonnes of CO2 annually—equivalent to the annual carbon emissions of South America. After a year in which net zero commitments gathered global momentum, coupled with renewed urgency for achieving energy security, the market outlook for the global wind industry looks even more positive. Approximately 557 GW of new capacity is expected to be added in the next five years under current policies. That is more than 110 GW of new installations each year until 2026. The energy of wind movement (wind flow), when absorbed by wind turbines, allows the generation of electricity by converting mechanical energy into electrical energy. Winds originate from uneven heating in the sun’s atmosphere, surface irregularities, and the Earth’s rotation. Terrain characteristics (e.g. roughness), environmental conditions (e.g. temperature), and buildings can change wind flow patterns. The process of converting wind energy (from the wind) into electrical energy, is defined as either wind energy or wind generation, both describe this task indistinctly. Wind turbines convert kinetic energy into mechanical energy and generators convert this mechanical energy into electrical energy [67]. For wind energy to be considered technically usable, its density must be greater than or equal to 500 W/m 2, at a height of 50 m, which requires a minimum wind speed of 7 to 8 m/s [68]. According to the World Meteorological Organization, in only 13% of the earth’s surface, the wind has an average speed equal to or greater than 7 m/s, at a height of 50 m. This proportion varies greatly between regions and continents, reaching 32% in Europe [68]. In order to ensure that the growth paradigm of the wind energy generation sector is sustainable, fair, and socially responsible, guaranteeing assumptions based on a clear and viable economic proposal, thus demonstrating the leading role of this way of generating energy. The wind turbines produced today, in addition to having several designs, provide a multitude of resources. In the literature, we find several classifications based on: rotor shaft alignment; used electric generator; operating rotation speed; power converter used and ability to develop control actions.

3.6.1

Classification of Wind Turbines—Rotor Shaft Alignment

We can classify modern wind turbines into two fundamental groups: the wind turbine with a horizontal-axis wind turbine (HAWT) (Fig. 15) and the vertical-axis wind turbine (VAWT) (Fig. 16). The working principle of wind turbines consists of transforming the kinetic energy of the air into the rotating mechanical power of the turbine rotor blades. Nowadays, the most common wind turbine is a horizontal axis propeller with 2 or 3 blades. Selecting the number of wind turbine blades is not an easy design choice. Three-blade systems cost more than two-blade systems, but two-blade wind generators need to operate at higher rotational speeds than three-blade ones. In this way, the individual blades of the two-blade wind generator need to be lighter and

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Fig. 14 Typical block diagram for a wind turbine power generation system

more uncomfortable and therefore more expensive. The basic formulation for the wind power at location A, perpendicular to the wind direction is given by Eq. 31: P=

1 ρ AC p v 3 2

(31)

where: P is the power, ρ is the air density, v is the wind speed and Cp is the power coefficient, which describes the fraction of the wind captured by a wind turbine. According to Betz rules, the value of the power coefficient features a theoretic limit connected with 59.7%. The assembly of wind generators seeks locations where wind energy is abundant (wind speed is higher at higher altitudes than close to the ground). Also, to avoid turbulence (change in wind speed when it hits obstacles), the location needs to be away from tall structures. Horizontal axis wind turbines collect wind energy against the actual direction associated with the wind [69]. The use of wind energy through a wind energy conversion system (WECS) is based on a structure composed of wind turbine blades, a generator, an electronic power converter, and a control system. Figure 14 indicates the block diagram involving different parts of WECS. There are different WECS options considering: (a) type of wind turbine (fixed or variable speed); (b) type of generator used (synchronous or asynchronous machines); (c) rotor positioning (horizontal or vertical) and (d) and control systems (stall or even pitch regulated). The primary objective of these systems is identical: to convert the kinetic energy of the wind into electricity and inject this energy into an electrical grid.

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Fig. 15 Mechanical structure of a HAWT

According to WWEA statistics in the world market, more horizontal wind turbines are used than vertical, with 1 in 5 manufacturers manufacturing vertical-type turbines. The main reason for the difference is the performance and cost-effectiveness advantages of horizontal wind turbines. • Horizontal-Axis Wind Turbines (HAWT) Depending on their application, high-power horizontal axis wind turbines are generally built in powers ranging from 2 to 8 MW. Currently, horizontal-axis wind turbines have the highest efficiency, being able to transform 40 to 50% of the kinetic energy received into electrical energy. Considering that it is the predominant wind turbine model for decades, the technology of horizontal axis wind turbines is at a high stage of maturity. Due to the height at which the nacelle is mounted, horizontal-axis wind turbines are able to receive the wind with greater speed. This means they are more likely to operate at higher wind speeds, which helps them deliver optimal performance. The main advantages of HAWT systems to be highlighted are: (a) its structure reaches greater heights which allows the turbine to be subjected to stronger winds; (b) its a high-efficiency turbine, as its blades receive energy from the wind continuously throughout its rotation, and (c) as the blades speed is practically constant during one rotation, the rapid fluctuations in electrical variables (voltage and reactive power) are negligible.

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Fig. 16 Mechanical structure of a VAWT

HAWTs systems have the following main disadvantages: (a) the weight of the equipment making up the nacelle requires that a tower be built to support its weight; (b) the weight of the generator, gearbox, transformer, and other devices inside the nacelle must be lifted during construction and for maintenance, and to keep up with the movement of the wind requires an additional yaw control system to turn the blades. Concerns about the environmental impacts of HAWT systems, such as noise emission, shading, and the effect on local ecosystems (e.g. when located on beaches, affect existing dunes) and wildlife (birds primarily), are still controversial and are widely discussed. Regarding installation standards, although governments around the world have established incentives for the expansion of generation from renewable sources, regulations for the installation of wind turbines may limit this expansion. The municipality must carry out a noise assessment, before authorizing the installation of a wind turbine in a certain area, to ensure that regulatory limits are observed.

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While there is usually a general rule of thumb, policies can still vary based on different local political, social, and natural environments. • Vertical-Axis Wind Turbines (VAWT) Vertical wind turbines are mainly used because they behave better in turbulent winds and emit low noise levels compared to horizontal axis wind turbines. Not least, the aesthetics of this type of turbine can be more attractive. For these reasons, these types of wind turbines are considered more appropriate for urban or semiurban regions. Three types of vertical-axis wind turbines: – Savonius: The predominant force in this type of generator is the drag force, that is, the turbines rotate predominantly by the air pressure on the blades. Savonius turbines are generally cheaper and start to spin at a slower speed compared to other types of wind turbines, but are the least efficient type of wind turbine considering the energy harvesting area and annual production of the same. – Darrieus: These are turbines with an aerodynamic profile designed in a similar way to the wings of an airplane, creating lift to move and generate energy. Darrieus wind turbines are more efficient than Savonius turbines. – Darrieus-Savonius: Hybrid wind turbine with Darrieus and Savonius systems coupled to the same shaft, which according to the manufacturers use the advantages of each type of turbine. VAWTs have the following advantages: (a) their structure (transformer, blades, generator, and gearbox) is mounted at ground level, which facilitates its installation and maintenance compared to HAWT systems; (b) not needing a yaw mechanism to direct the blades into the wind is an advantage for locations with varying wind directions; (c) for urban applications, its aesthetic impact is lower than for HAWT systems and (c) the starting speed (start of generation) of VAWT is generally lower than that of HAWT. The disadvantages of a vertical axis wind turbine are: (a) lower performance compared to a horizontal axis wind turbine; (b) the support towers are low, reducing the use of higher wind speeds; (c) close to the ground and as a result of the presence of other objects or obstacles, they can cause turbulence in the wind flow, introducing mechanical vibrations in the turbine components, shortening their useful life; (d) it’s enormous inertia can result in the need for an external power source to start the turbine, and (e) mathematical modeling (aerodynamics) is very complex, making design difficult.

3.6.2

Classification of Wind Turbines—Operating Rotation Speed

With regard to operational rotational speed, wind turbines can be divided into two classes: fixed-speed wind turbines (FSWT) and variable-speed wind turbines (VSWT).

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Fig. 17 Torque-speed characteristics of a fixed-speed wind turbine

• Fixed-Speed Wind Turbine—FSWT Because they are simpler to build and operate than VSWT systems, older systems were primarily of the FSWT type. According to their principle of operation, they generate electricity only when the generator shaft rotates above its synchronous speed, i.e. for high wind speeds. Although they are cheaper and require less maintenance than variable speed types, they are limited in their power generation due to these operational limitations. The generator operates in an almost linear range from point 1 to point 2, as shown in Fig. 17. Before point 1, the speed of the turbine is below the cut-in speed. When the speed is higher than that, at point 2, the machine enters into its unstable nonlinear region. Because of the steep slope of the characteristic, the range of speed is very narrow, thus it is called a “constant” or “fixed-speed turbine”. The range of the developed power (shaft power of the generator) in this system is obtained by Eq. 32: Por t− f s = Pn 2 − Pn 1 = Tn 2 ωn 2 − Tn 1 ωn 1

(32)

where: Por t− f s —power in the operation region speed for fixed speed; Pn 2 , Pn 1 — power at speed points n1 and n2; Tn 2 , Tn 1 —torque at speed points n1 and n2; ωn 2 , ωn 1 —angular speed at speed points n1 and n2. If we consider ωn 2 ≈ ωn 1 , then Eq. 32, can be simplified Por t = (Tn 2 − Tn 1 )ωn 1

(33)

Using pitch angle control, and adjusting lift force, we can control output power.

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Fig. 18 Torque-speed characteristics of the variable-speed wind turbine

• Variable-Speed Wind Turbine—VSWT The use of electronic power converters that allow Variable-Speed Wind Turbine— VSWT systems to generate electrical energy at a wide range of speeds, even at speeds below synchronous speed, makes these systems more complex. Its operating range is much wider than that of FSWT systems. The torque-speed characteristics of the VSWT are shown in Fig. 18. To achieve these characteristics, a voltage is injected into the generator rotor circuit. If the operating torque is between points 1 and 2 (Fig. 18) the speed of the generator has a wide range as ω2 > ω1. The range of power for this system is obtained by Eq. 34 Por t−vs = Pn 2 − Pn 1 = Tn 2 ωn 2 − Tn 1 ωn 1

(34)

where: Por t−vs —power in the operation region speed for variable speed; Pn 2 , Pn 1 — power at speed points n1 and n2; Tn 2 , Tn 1 —torque at speed points n1 and n2; ωn 2 , ωn 1 —angular speed at speed points n1 and n2. As a result of the wide operating speed Por t−vs > Por t− f s . With VSWT wind systems we have the possibility of controlling the flows of active and reactive power and, consequently, reducing any problems caused by fluctuations in the primary energy sources. These systems have low torque peaks in the mechanical structure in addition to having cheaper mechanical systems, with cheaper gears. Since they use power converters to connect to the electrical grid, mechanical damping systems are unnecessary, since the electrical interface can provide the necessary damping. They have greater energy efficiency, due to the optimal use of the mechanical characteristics of the turbine and the generation of electrical energy with quality compatible with conventional generation systems.

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Fig. 19 Comparative torque-speed characteristics of variable-speed and fixed-speed wind turbine

The problems of synchronization with the electrical grid, common in other wind generation systems are avoided, resulting in greater operational flexibility in the starting and braking processes. The generic power-speed characteristics of constant and variable speed turbines can be seen in Fig. 19. For FSWT systems, the cutting speed (minimum speed for generating electricity) is greater than the synchronous speed of the generator, and its cutting speed is determined by the maximum turbine speed at point 2 of Fig. 17. VSWT systems, on the other hand, operate with a cutting speed lower than the synchronous speed and their cutting speed is higher than that of the FSWT (see Fig. 18). Regarding the rated power, it is lower in FSWT systems compared to VSWT systems, considering that in these the output power is supplied by the stator and rotor. The stator can still deliver its rated power and the rotor provides extra power.

4 Conclusion The chapter is to identify and map/chart the technologies for energy storage systems (ESS) available and qualitatively evaluate their applicability in the power system considering its impact on the quality and reliability of electric power system (EPS) [70, 71]. Energy Storage Systems are used to provide electrical energy support for applications in RES. However, BESS is classified according to the form of energy storage as: mechanical energy, kinetic or chemical energy, etc. [32, 37, 38, 72]. Recent research highlight the importance of the integration and use of BESS for the consolidation of Smart Grids [37, 70]. Table 2 presents a roadmap of applications,

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Table 2 Characteristics of ESS applications Rated power (MW) Bulk energy

Ancillary service

Transmission infrastructure Distribution infrastructure Customer energy management service

Renewables integration

Electrical energy time-shift Power supply capacity Load following Regulation Frequency response Reserve Voltage support Black star Upgrade deferral Upgrade deferral Power quality

Power reliability Retail electricity time-shift Demand charge management Smoothing output

Duration

Response

Cycle/yr

0.1–500

2–6 h

mseg–min

300–400

1–500

2–6 h

min

50–365

1–100

15 min–1 h

∼1 s

250–10000

10–40 10–40

s–h 2 min–1 h

∼1 s ms

250–10000 1000–10000

10–100 1–10 MVAr

min–h min–1 h

ms s

10–1000 N/A

5–50 10–100

s–h 1–8 h

min s

10–20 10–50

0.4–10

1–4 h

s

50–365

0.1–10

ms–15 min

ms

100–10000

0.05–10

1–8 h

ms

U4 .

In [28], a local method based on the reduction of generated active power is proposed, where inverters reduce the active power production based on the voltage at

Fig. 2 P(U) method

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their PCC. As consumers are monetarily compensated by the generation of active power in some countries, such as Brazil [29], a voltage control totally based on the reduction of active power should discourage consumers to install PV systems.

2.2 Q(U) Method In the Q(U) method, the amount of reactive power injected or absorbed by an inverter depends on the level of voltage at its PCC [30]. Figure 3 shows the droop control curve used in the Q(U) method, which is represented by (2). ⎧ ⎪ ⎨0,

U < U1 Q= × (U − U1 ) U1 < U < U2 ⎩ −Q max , U > U2 . Q max ⎪ U1 −U2

(2)

Proper management of reactive power can be used to aid in voltage control [31, 32], mainly, when the injected power in the network is moderate. However, the application of this method can face capacity limitations at noon (when active power production is high). This limitation is due to the small spare of reactive power capacity in inverters to properly deal with the overvoltage. The same limitation occurs when the generated power is higher than consumers’ demand [23, 30], because of the high ratio of resistance to reactance of LV networks.

2.3 Q(P) Method In the Q(P) method, the amount of reactive power injected or absorbed by an inverter is determined as a function of the available active power (Pava ) [33]. Figure 4 shows the droop control curve used in the Q(P) method, which is represented by Eq. (3).

Fig. 3 Q(U) method

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Fig. 4 Q(P) method

Q=

⎧ ⎪ ⎨0,

Pava < P1 × (Pava − P1 ), P1 < Pava < P2 ⎩ −Q max , Pava > P2 . Q max ⎪ P1 −P2

(3)

According to IEEE-1547, the maximum quantity of reactive power can be used to aid in voltage control [31, 32], mainly, when the injected power in the network is moderate. This control method has the same limitations as the Q(U) method.

2.4 cosϕ( P) Method In the cosϕ(P) method, the inverter power factor (cosϕ) is determined as a function of the available active power (Pava ) [23]. Figure 5 shows the droop control curve used in the cosϕ(P) method, which is represented by Eq. (4). ⎧ ⎪ Pava < P1 ⎨1, 1− p f min cosϕ = (4) × (Pava − P1 ) + 1, P1 < Pava < P2 P −P ⎪ ⎩ 1 2 p f min , Pava > P2 .

Fig. 5 cosϕ(P) method

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Fig. 6 Q(U)&P(U) method

According to the regulatory energy legislation, a minimum value of the inverter power factor can be established and should be applied in the cosϕ(P) method.

2.5 Q(U)&P(U) Method In the Q(U)&P(U) method, each inverter power factor considers both the Q(U) method and the P(U) method described in Sects. 2.1 and 2.2. Figure 6 shows the voltage control curves of the Q(U)&P(U) method. The Q(U)&P(U) method is as effective as the Q (U) method in reducing overvoltage at low and medium levels of PV penetration. For high levels of PV penetration, the Q(U)&P(U) method has the best performance at the expense of active power reduction from inverters.

3 Illustrative Example To better understand how the voltage control methods presented in Sect. 2 work, the short LV feeder shown in Fig. 7 is considered. This feeder has four residential consumers. For simplicity, it is assumed that all consumers have the same demand profile, which is shown in Fig. 8a.

Fig. 7 Short LV feeder with four consumers

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(a) Consumers’ load demand

(b) Power output of 5 kW PV system

Fig. 8 Load demand and PV power generation

3.1 PV Generation To obtain PV power generation, it is assumed that all residential PV systems have the same capacity. Since the network under study is a short one, it is considered that each PV panel receives the same solar irradiance. The considered solar irradiance profile presents a peak of 0.765 kW/m2 . Figure 8b shows the active power generated by a 5 kW PV system.

3.2 Operational Limits The bounds of the service voltage in steady-state are assumed Um in = 0.92 p.u. and Um ax = 1.05 p.u. Exceeding values occur if the nodal voltages are between 1.05 and 1.06 p.u., or between 0.87 and 0.92 p.u., for more than 3% of the monitored period (timespan not necessarily consecutive of 15 min in a 24-h measurement). This also occurs if any voltage is above 1.06 p.u. or below 0.87 p.u., in any 15-min timespan.

3.3 Scenarios Definition To identify the influence caused by the insertion of PV systems and the application of the local voltage control methods, three cases are considered: Base case, PV case, and Inverter-control case. The Base case represents the network without the installation of PV systems. The tap of the distribution transformer is set to 1.01 p.u. to prevent under-voltage problems. In the PV case, the network is analyzed considering PV penetrations (αi %) of 25% and 50% to identify which buses are more sensitive to overvoltage. PV inverters

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operate with a unity power factor, and the distribution transformer presents the same transformer tap setup mentioned above. In the Inverter-control case, inverters are set according to any of the presented voltage control methods. These set take into account the minimum value of inverter power factor (0.95) established for the distribution system operator and the recommendation given by the IEEE 1547 standard. To better evaluate the performance of the voltage control methods, the participation of PV inverters during voltage regulation is only taken into account for αi % = 25%, 50%, 75% and 100%.

3.4 Voltage Impact Caused by PV Generation Figure 9 shows the voltage profile at buses 1 and 4 for the Base and PV cases. Figure 9a, b are results for bus 1, whereas Fig. 9c, d for bus 4. When comparing voltages of the PV and Base case, it is observed that the voltage rise is higher when PV is located at the end of the network (Fig. 9c, d) than when

(a) PV system at bus 1

(c) PV system at bus 4

Fig. 9 Voltage profile

(b) PV system at buses 1 and 2

(d) PV system at buses 3 and 4

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it is located at the beginning of the network (Fig. 9a, b). Voltage changes related to the Base case occur at hours with power generation. Higher voltage increases are observed at hours of high power generation (from 11 am to 2 pm). This voltage rise behavior is in accordance with results found in the specialized literature.

3.5 Voltage Increase Mitigation Using Inverters In Sect. 3.4 it was concluded that bus 4 presents the highest voltage increase. Therefore, the voltage analysis of this subsection focuses on bus 4. The setting parameters of the voltage control methods were set considering recommendations given by IEEE-1547 [16] and the minimum value of inverter power factor (0.95) established for the distribution system operator. The considered values of these parameters are indicated in Table 1. Figure 10 shows the voltage profile at bus 4 around noon considering the application of the voltage control methods described in Sect. 2. The horizontal red line in Fig. 10 represents the upper voltage limit. The P(U) method is not activated for αi = 25% of PV penetration since the voltage at bus 4 does not surpass 1.05 p.u. Only the Q(U), Q(P) and Q(U)&P(U) methods eliminate the overvoltage at bus 4 when α = 50%. With α = 50%, the cosϕ(P) method only fails to mitigate the overvoltage at t = 12 h. From αi = 25% to αi = 75%, the Q(U) and Q(U)&P(U) methods present the same performance to mitigate overvoltage. the order of better performance to mitigate the overvoltage is: (i) Q(U)&P(U) method, (ii) Q(U) method, (iii) Q(P) method, (iv) P(U) method and (v) cosϕ(P) method. When αi = 100%, all control methods fail to eliminate the overvoltage. Therefore, utilities must consider the use of complementary voltage regulation equipment for high levels of PV penetration.

Table 1 Setting parameters of local voltage control methods Method U1 U2 P1 P2 U3 P(U) Q(U) Q(P) cosϕ(P) Q(U)&P(U)

– 1.02 – – 1.02

– 1.05 – – 1.05

– – 0.5 0.5 –

– – 1.0 1.0 –

1.05 – – – 1.05

U4

pfmin

r

1.06 – – – 1.06

– – – 0.95 –

0.5 – – – 0.5

Inverter-Based Local Control Methods …

(a)

= 25%

(c)

= 75%

207

= 50%

(b)

(d)

= 100%

Fig. 10 Voltage profile at bus 4 considering voltage control methods

4 Performance of the Voltage Control Methods In LV networks, the decision to install PV systems is usually made by consumers; consequently, the sizes and locations of PV systems are uncertain during the integration of PV systems into the network. Thus, the effectiveness of voltage control methods to regulate voltage in the network should be evaluated considering all possible integration scenarios for PV systems. In extensive networks, the number of possible scenarios is large and the evaluation considering all the PV systems’ integration scenarios can become impractical [34]. To address this issue, the Monte Carlo simulation is used to create a number of scenarios, N, lower than the total number of possible PV systems’ integration scenarios. Several definitions of PV penetration have been found in the specialized literature [35]. In this way, the PV penetration (α) is considered as the ratio between the number of consumers with PV systems and the total number of consumers connected to a network. Since more than one PV integration scenario can take place at a specific level of PV penetration (αi %), the performance of the voltage control methods at a specific level of PV penetration is determined in terms of the probability of overvoltage using (5).

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P(U M >= Umax | α = αi %) =

nU N

(5)

In (5), U M is the voltage (p.u) at the bus with the highest voltage. Umax is the upper bound of the allowable voltage range (p.u.), while αi % is the level of PV penetration. The number of scenarios with overvoltage is represented by n U . The criterion for determining N is that the probability calculated using this value presents small changes compared to the probabilities obtained with higher numbers scenarios. The proposed method performs well at a specific level of PV penetration if the probabilities calculated using (5), respectively, are lower than 5%. This tolerance is commonly used in statistical analysis to make an inference from a sample [36], that is, the set of N PV system integration scenarios.

5 Application Study The estimate of the PV potential per sub-areas of a Brazilian city is carried out by [37]. Here, an LV feeder located in the subarea with the highest PV potential is analyzed. Figure 11 shows the single-line diagram of the selected feeder which is an LV feeder with three phases of four wires (3F + N). Three-phase, 11.4 kV / 220127 V distribution transformer of 45 kVA couples the selected LV network to the medium-voltage network. The steady-state service voltage limits, established by the regulatory agency, are Um in = 0.92 and Um ax = 1.05 [26]. Exceeding values occur if the nodal voltages are between 1.05 and 1.06 p.u., or between 0.87 and 0.92 p.u., for more than 3% of the monitored period (timespan not necessarily consecutive of 15 min in a 24-h measurement). This also occurs if any voltage is above 1.06 p.u. or below 0.87 p.u., in any 15-min timespan. The selected network presents eight splices, which are joined by three-phase conductors of 95 mm2 . The distance between two adjacent splices is 40 m. In total, there

Fig. 11 LV network under analysis

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(a) Consumers’ load demand

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(b) Power output of PV systems

Fig. 12 Load demand and PV power generation

are 30 residential consumers located in nodes, as shown in Fig. 11. The consumer nodes are connected to the network using one phase and a neutral conductor (1F + N) of 35 mm2 . The distance between a splice and a consumer node is 20 m. There are four types of residential consumers: A, B, C and D, which load demand is shown in Fig. 12a. These consumers were classified following the methodology presented in [38] and using measurements of monthly energy consumption. A power factor equal to 0.95 is assumed for each consumer’s demand. Statistics of installed PV systems’ capacities in the study city [39] are considered during the generation of PV systems’ integration scenarios. The most common installed capacities in this subarea are between 2 kW and 6 kW. Additionally, during the selection of PV system capacities, it is taken into account that consumers cannot install PV systems with capacities higher than their contracted power [40]. Local data of solar radiation and temperature [41], as well as the model presented in [33], integrates the simulation of power generation by assuming all PV systems are subjected to the same weather conditions. The power output of the considered PV systems are shown in Fig. 12b. Three study cases are conducted: Base case, PV case and Inverter-control case, which are similar to those described in section Sect. 3.3. The difference corresponds to the levels of PV penetration analyzed in this section. In the PV case, only PV penetration levels from 10% to 60% are considered. This consideration is based on the low performance of the voltage control methods observed in Sect. 3.5 for higher levels of PV penetration. In the Inverter-control case, the P(U) method was excluded due to its low performance to mitigate the overvoltage. Therefore, in the Invertercontrol case are considered the following voltage control methods are: Q(U), Q(P), cosϕ(P) and the Q(U)&P(U). In addition, in this section, the same inverter settings indicated in Table 1 of Sect. 3.5 are considered. Due to uncertainties in the locations and capacities of PV systems during their integration into the network, 200 PV systems integration scenarios are considered at each level of PV penetration to study the cases. For each scenario, power flows are performed to obtain voltage profiles at all consumers’ nodes. Then, the probability of overvoltage is computed according to (5). Consideration of a larger number of

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scenarios results in probabilities that present small changes (less than 1%) for the network under analysis. Power flows are carried out to simulate real-time network operation during the application of voltage control methods in each scenario. Due to data availability, power flows are conducted with a one-hour resolution.

5.1 Voltage Analysis The probability of overvoltage at all nodes with αi = 20% and 30% is shown in Fig. 13. These probabilities are calculated using (5) and represent the percentage of scenarios in which each node has an overvoltage problem. With αi %=20%, the probabilities of overvoltage are low occurring in few nodes. The number of nodes with overvoltage increases when αi = 30%. In Fig. 13, it is observed that the overvoltage problem from the PV generation is more common to nodes located at the end of the feeders (e.g., nodes #14 and #15) than those located close to the transformer (e.g., nodes #1 and #2). The hourly voltage profile at node #14 in the Base case and in the PV case with αi = 10% up to 60% is shown in Fig. 14. Each solid green curve represents the voltage profile at node #14 in one of the 200 simulated scenarios. In the Base case, there is no voltage problem at node #14. There is a margin between the voltage profile and the allowable voltage range (0.92 p.u. and 1.05 p.u.). Therefore, the transformer tap setup ensures the voltage quality against possible voltage fluctuation in the medium-voltage. Figure 14 shows that the voltage at node #14 with αi = 20% exceeds the upper voltage limit at 12 h. However, the overvoltage lasts 6 h (from 10 to 16 h) in one of the simulated scenarios for αi = 50%. This results in an overvoltage duration index of 27%, which is higher than the limit of 3%

Fig. 13 Probability of overvoltage of nodes in the PV case

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(b) Voltage profile at node #14

(c) Voltage profile at node #14

Fig. 14 Hourly voltage profiles at node #14 in the Base and in the PV cases

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established by the regulatory agency [26]. The utility must be monetarily charged if no voltage control actions are taken. The hourly voltage profile at node #14 in the Base case and in the Inverter-control case considering the voltage control methods for αi = 30% and 50% is shown in Fig. 15. Each black curve represents the voltage profile at node #14 in one of the 200 simulated scenarios. Figure 15 shows that except for the cosϕ (P) method, all other voltage control methods properly mitigate overvoltage when αi = 30%. With αi = 50%, the overvoltage lasts 5 h (from 10 to 15 h) when the cosϕ(P) method is applied. This results in an overvoltage duration index of 22%, which is higher than the limit of 3% established by the regulatory agency [26]. For the other voltage control methods, the overvoltage lasts 1 or 2 h in few scenarios when αi = 50%, being the Q(U)&P(U) method which present the best performance. Figure 16 shows the probability of overvoltage after the application of the voltage control methods. The probability of overvoltage in the PV case is included as well. The node with the highest probability of overvoltage (node #14) is analyzed for each level of PV penetration. The probability of overvoltage is calculated using (5) and a tolerance of 5% is assumed based on Sect. 4. In Fig. 16, the PV hosting capacity of the network is 25% in the PV case. The application of the cosϕ(P) method is able to improve the PV hosting capacity of the network up to 35%. The application of the Q(P) method improves the PV hosting capacity of the network up to 55%, whereas the application of the Q(U) and Q(U)&P(U) methods enhances the PV hosting capacity of the network up to 56% and 57%, respectively. Therefore, the Q(P), Q(U) and Q(U)&P(U) method relieve power capacity allowing the installation of more PV systems in the network with a probability of overvoltage lower than 5%. Since the application of the Q(U), Q(P), cosϕ(P) or Q(U)&P(U) method is based on the use of reactive power or active power, it is important to know how it impacts on network’s operation efficiency. In the next subsection, the active losses of the lines are calculated considering αi = 50%.

5.2 Lines’ Active Losses Lines’ losses in the network depend on the power flow through the lines. Therefore, these losses should change when the Q(U), Q(P), cosϕ(P) or Q(U)&P(U) method is applied, since these methods are based on reactive or active power. Figure 17 shows the average values of lines’ active losses in the Base case, PV case and when the voltage control methods are applied. Active losses in the network increase when inverters operate with a unity power factor in the PV case. The application of the voltage control methods increases still more these losses. The Q(P) method results in the highest values of daily active losses followed by the Q(U) and Q(U)&P(U) methods. The application of the cosϕ(P) method results in the lowest active losses compared to the other methods. Losses

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(a) Voltage profile at node #14

(b) Voltage profile at node #14

(c) Voltage profile at node #14

(d) Voltage profile at node #14

(e) Voltage profile at node #14

(f) Voltage profile at node #14

(g) Voltage profile at node #14

(h) Voltage profile at node #14

Fig. 15 Hourly Voltage profile at bus 14 considering voltage control methods

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Fig. 16 Overvoltage probabilities of the voltage control methods

Fig. 17 Average of active losses of lines with α = 50%

increase in hours of high generation (11 h to 15 h) and decrease in hours of low generation (from 7h to 9h and from 16h to 19h) compared to the Base case. Daily lines’ active losses in the Base case are 3.81 kWh. In the PV case, losses are 141% of the Base case. When Q(U), Q(P), cosϕ(P) or Q(U)&P(U) are applied, losses are 159%, 157%, 145% and 159% of the Base case, respectively. The cosϕ(P) method results in lower daily lines’ active losses. A comparison between Figs. 16 and 17 shows that the evaluation of control methods solely based on voltage mitigation effectiveness can hide other negative aspects. For example, in Fig. 16 it was observed in Fig. 16 that the Q(U) and Q(U)&P(U) methods present a better performance for voltage mitigation. However, according to Fig. 17, these methods result in higher lines’ active losses.

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Fig. 18 Expected value of power factor at the input of the feeder

5.3 Power Factor at the Beginning of the Feeder During the calculation of losses in medium-voltage networks, Brazilian utilities generally assume that distribution transformers operate with power factors equal to or above 0.92. Therefore, it is important to know how the transformer’s power factor changes when PV systems are connected to LV networks. Figure 18 shows the expected value of the power factor at the beginning of the feeder. In the Base case, the power factor is around 0.94. In the PV case and the Invertercontrol case, the power factor decreases in the periods from 6 h to 11 h and from 15 h to 19 h. However, in the PV case, the power factor is improved from 11 h to 15 h, while in the Inverter-control case, the power factor is even further reduced. Moreover, notice that for all methods, there are two instants (at hours 9 and 16) at which the power factor reaches really low values (around 0.4). The explanation for these low values is because in those hours, demand and generated active power have similar values. Thus, most of the active power from the network to the buses with PV systems is nearly zero while the reactive power has little change, which results in low values of power factors. However, as active power generation increases and demand remains relatively low (e.g. between hours 11 and 15), the power factor improves. Notice that this section is related to the power factor at the input of the feeder and not to the power factor of inverters. For each inverter, the proposed method guarantees its operation within the allowable limits (above 0.95).

5.4 Transformer Loading The expected value of the transformer loading is shown in Fig. 19. It is observed that there is no transformer overloading in the Base case. Moreover, transformer loading presents similar behavior that lines’ active losses in the PV and Inverter-control cases.

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Fig. 19 Expected value of transformer loading

It is essential to mention that Fig. 19 only indicates the expected value of the transformer loading and not the actual value observed in each scenario. Therefore, there are scenarios where the transformer loading is below or above this expected value. The results obtained indicate that up to αi = 60%, the application of the voltage control methods do not result in any scenario with transformer overloading between 100% and 120%, except for the Q(P) method. This method presents 28 scenarios with transformer overload. Therefore, assuming a 5% of tolerance to the probability of overvoltage, the Q(U) and Q(U)&P(U) methods will have a good performance to mitigate the overvoltage for αi = 55% (see Fig. 16) without overloading the transformer.

6 Conclusion Inverters can be employed for mitigating overvoltage caused by PV generation. Due to uncertainties in the location and sizes of PV systems, several scenarios of PV integration should be considered in planning studies. The results obtained from the application to a Brazilian LV network with only residential consumers indicate that the Q(U) and Q(U)&P(U) methods provide better voltage regulation than similar local voltage control methods. As consequence, it enables higher levels of PV penetration in the network. It was observed that at low levels of PV penetration, the Q(P) method needlessly absorbs excessive reactive power to mitigate the overvoltage, which resulted in higher active losses, lower values of power factor at the input of the feeder, and a higher number of scenarios with transformer overloading. Daily lines’ active losses in the network under study are higher in the PV case than in the Base case. However, an increase or a reduction of active lines losses could occur depending on demand and generation characteristics.

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For networks with other allowable voltage ranges, other values should be used for the setting of the inverter parameters to obtain proper voltage regulation. Acknowledgements This study was financed by Programa de Incentivos a las Publicaciones Científicas de la Universidad Tecnológica del Perú; by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001; by São Paulo Research Foundation Brazil (FAPESP) under grants:2015/21972-6, 2019/00466-6 and no 2021/08832-1; by Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil (CNPq) undergrant: 422044/20180, 310299/2020-9 and 408898/2021-6; by Instituto Nacional de Ciência e Tecnologia de Energia Elétrica - Brazil (INCT-INERGE); and by ENEL undergrant: APLPEE-00390-1062/2017 - P&D00390-1083-2020_UFABC. The authors also thank the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Peru (CONCYTEC) for the access to papers published in international journals.

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Variable-Speed-Driven Three-Phase Surface-Mounted Permanent Magnet Synchronous Machine Applied to Wind Generation Systems José Roberto Boffino de Almeida Monteiro and Stefan Thiago Cury Alves dos Santos

Abstract Wind generation has been shown to be a suitable alternative source of energy in the context of smart grids. Among the machines used in wind generation systems, the permanent magnet synchronous machine (PMSM) stands out for its high power density, high efficiency, absence of rotor windings, among others. This chapter is dedicated to the study of the variable-speed-driven three-phase surfacemounted permanent magnet synchronous machine (SMPMSM) applied to power generation. The machine model is presented in three-phase, stationary orthogonal αβ and synchronous orthogonal dq coordinate systems, as well as the waveforms of the back electromotive force (back-EMF) per phase in each coordinate system. In the case of SMPMSMs with a non-sinusoidal back-EMF waveform, the dqx transformation is applied in order to reduce the electromagnetic torque ripple. Based on the machine model in an appropriate reference frame, it is possible to design a decoupled control of torque; as an example, the proportional-integral controller is adopted. In addition, to eliminate the need to use axis angular position sensors or to increase the redundancy, the concepts of rotor position estimation are introduced, and a strategy presented is the use of the Kalman filter together with phase-locked loops. Finally, a study using data from a real machine, employed in wind turbines, exemplifies the concepts covered in this chapter.

J. R. B. de A. Monteiro (B) · S. T. C. A. Santos São Carlos School of Engineering, Avenida Trabalhador São-carlense, 400, São Carlos, SP, Brazil e-mail: [email protected] S. T. C. A. Santos e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_8

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1 Introduction In recent years, there has been a growing interest on the part of developed countries in relation to smart grids [1]. Smart grids can provide more adequate control of the electricity consumption pattern, provide more security for the system and more efficient operation, reducing losses. In addition, the great advance in the area of information technology in smart grids has made possible an uninterrupted supply of electricity to consumers, and a high level of insertion of distributed generation in the grid [2]. The insertion of distributed generation contributes to better performance and more efficient operation of the network. An example is the hybrid solar-wind system that uses both energy sources and proves to be reliable under different weather and climatic conditions, since they complement each other, and the weakness of one is the strength of the other [3, 4]. In the case of wind generation, which is the object of study in this chapter, a turbine coupled to an electric generator captures the kinetic energy of the winds and converts it into electrical energy. Based on the turbine’s dimensions and its operating range, there are different electrical machines that can be applied. The doubly-fed induction machine is the most commonly employed, however there is an increasing use of the permanent magnet synchronous machine (PMSM) in which the magnets are mounted on the surface of the rotor [5]. In the case of the surface-mounted permanent magnet synchronous machine (SMPMSM), there is no need for excitation, unlike the rotorwound synchronous machine and the doubly-fed induction machine, so there is no need for brushes, which implies maintenance reduction. Variable-speed-driven SMPMSMs applied to wind generation systems require full converters [6]. A very common structure consists of the use of two two-level voltage source converters in the back-to-back configuration, with capacitors in the DC link. Such configuration allows independent control of amplitude and frequency in both three-phase terminals, allowing the machine variable speed drive (through the Machine-Side Converter - MSC) and power delivery to the electrical network (through the Grid-Side Converter - GSC). Figure 1 exemplifies the back-to-back converter structure applied to wind power generation systems with SMPMSMs. This chapter aims to develop a theoretical base for applying SMPMSMs in wind power generation systems, and thereafter, applies this theory to a power generation system tested with computational simulation. By the end of this chapter, the reader should be able to understand the SMPMSM modeling and the implications of choosing the reference frame on the control of the machine and its operation. Brief considerations on rotor position estimation and connection to the electrical grid are also presented.

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Fig. 1 Wind power generation system with SMPMSM and back-to-back converter

2 Three-Phase Surface-Mounted Permanent Magnet Synchronous Machine Modeling The mathematical model of the SMPMSM is necessary for studying the operation and control of this machine. Unless otherwise stated, the following theoretical considerations in this Section are based on [7], where some hypotheses are considered: 1. Reluctance variations due to the rotor position are negligible. 2. The magnetic and electrical properties of the used materials are linear. 3. There is symmetry between the phases of the machine; counter-electromotive forces (back-EMFs), resistances, self-inductances, and mutual inductances per phase are equal to each other. The electrical equations of a wie-connected SMPMSM is: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ vm,a i m,a L s Ms Ms ea vm,n i m,a d ⎣vm,b ⎦ = Rs ⎣i m,b ⎦ + ⎣ Ms L s Ms ⎦ ⎣i m,b ⎦ + ⎣eb ⎦ + ⎣vm,n ⎦ , vm,c i m,c Ms Ms L s dt i m,c ec vm,n

(1)

where vm,a , vm,b , vm,c are the phase terminal voltages; i m,a , i m,b , i m,c are the stator phase currents; ea , eb , ec are the back-EMFs; vm,n is the voltage of the stator neutral terminal; Rs is the stator resistance; L s is the stator auto-inductance; and Ms is the mutual inductance between two stator phases. The deduction of this model is in [7]. The counter-electromotive forces per phase, induced when there is movement in the machine rotor, are the result of the interaction between the field generated by the rotor magnets and the stator windings, ra , r b , r c . It is noteworthy that ra , r b , r c are function of the rotor position:

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⎡ ⎤ ⎤ ⎡ ⎤ ⎡ ea ra ra d d ⎣eb ⎦ = ⎣r b ⎦ dθr . ⎣r b ⎦ = dt  dθe  dt ec rc rc

(2)

However, the time derivative of θe equals the machine electrical frequency ωe . Furthermore, one can define ⎡ ⎤ ⎡  ⎤ ⎡ ⎤   Fa d ⎣ ra ⎦ ⎣ ra r b = r b ⎦ = m ⎣ Fb ⎦ , (3) dθe   F rc

rc

c

so that the back-EMF can be described as ⎡ ⎤ ⎡ ⎤ ea Fa ⎣eb ⎦ = ωe m ⎣ Fb ⎦ , ec Fc

(4)

where Fa , Fb , Fc are the normalized back-EMF waveforms,whose amplitude is unitary; and m is the back-EMF amplitude normalized by the machine electrical frequency. Finally, being n p the number of pole pairs of the SMPMSM, the electromagnetic torque developed by the machine is:   Tel = n p m Fa i m,a + Fb i m,b + Fc i m,c .

(5)

The back-EMF waveform has significant impact on machine operation and control. The two most common back-EMF waveforms are sinusoidal and trapezoidal, which will be discussed next. SMPMSM with sinusoidal back-EMF The normalized ideal sinusoidal back-EMF waveforms are presented in Fig. 2, and is defined as: Fa = − sin θe Fb = − sin (θe − 2π/3) Fc = − sin (θe + 2π/3).

(6)

SMPMSM with non-sinusoidal back-EMF Non-sinusoidal back-EMF waveforms are usually modeled and represented as ideal trapezoidal signals with flat top regions of 120 degrees or 2π/3 rad, as depicted in Fig. 3. The normalized ideal trapezoidal back-EMF per phase is described as: Fa = −trap120 θe Fb = −trap120 (θe − 2π/3) Fc = −trap120 (θe + 2π/3).

(7)

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Fig. 2 Sinusoidal back-EMF waveform

Fig. 3 Trapezoidal back-EMF waveform

However, any non-sinusoidal back-EMF waveforms are possible in real machines and can be considered, not only the ideal trapezoidal signal.

2.1 SMPMSM Modeling in Stationary Orthogonal Reference Frame A common strategy to reduce model complexity is to change its reference frame [8]. In electrical systems, it is usual to look for orthogonal references to the detriment of the three-phase reference: taking into account the mathematical principles that surround electrical engineering, the three-phase values of electrical machines can be expressed with complex vectors, which can be represented in a Cartesian system to express the physical relationships of the machine [9]. The electrical variables in

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Fig. 4 Graphical representation of the stationary orthogonal reference frame (αβ) along with the three-phase axes [7]

orthogonal reference frames are 90 degrees from each other instead of 120 degrees as in the three-phase notation. Arbitrary three-phase values can be described on a stationary orthogonal reference frame xα , xβ , x0 , as depicted in Fig. 4. For this, it is used the αβ transformation. There are two major variations of such transformation: the amplitude-invariant, also known as the Clarke transformation [10], and the power-invariant transformation, also known as Concordia transformation [11], which will be adopted in this chapter: ⎡ ⎤  xa

2 1 e j2π/3 e− j2π/3 ⎣ xb ⎦ xαβ = (8a) 3 x c

√

3 1 x0 = 3

1

⎡ ⎤

xa 1 ⎣xb ⎦ , xc

(8b)

where j is the complex operator and xαβ = xα + j xβ is the vector representation of x in the αβ axes. Therefore, the SMPMSM model in stationary orthogonal reference frame is deduced by applying the transformation (8) to (1): dim,αβ + ωe m Fαβ dt

(9a)

√ di m,0 + ωe m F0 + 3vm,n . dt

(9b)

vm,αβ = Rs im,αβ + (L s − Ms ) vm,0 = Rs i m,0 + (L s + 2Ms )

Similarly, the electromagnetic torque developed by the machine, from (5), is described as:   (10) Tel = n p m Fα i m,α + Fβ i m,β + F0 i m,0 .

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Fig. 5 Back-EMF waveforms in stationary reference frame

Figure 5 shows the back-EMF waveforms in stationary reference frame for both sinusoidal (a) and trapezoidal (b) back-EMF waveforms. The forms kept similarity, in the α and β axes, concerning the waveforms in the three-phase reference frame. However, there is no zero component for the sinusoidal back-EMF, while there is a zero component for the trapezoidal back-EMF. As a consequence, if the zero component of the current is controlled, it is possible to increase the electromagnetic torque delivered by the SMPMSM with trapezoidal back-EMF, according to (10). The drawback is the need in controlling the neutral current, which will not be considered in this chapter. The α and β components of the back-EMF waveforms can be plotted parametrically (Fig. 6). The sinusoidal back-EMF waveform generates a circle with a radius

Fig. 6 Parametric plot of back-EMF waveforms in stationary reference frame

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√ of 3/2, √ and the trapezoidal back-EMF waveform generates a regular hexagon of side 2 2/3.

2.2 SMPMSM Modeling in Synchronous Orthogonal Reference Frame A way to simplify the control of electrical machines is to define a reference frame where electrical variables are described as DC components. It is also convenient to equate the electromagnetic torque as a function of only one current component, which favors the developing of a desired electromagnetic torque with minimum stator current and, consequently, facilitates the controller design. The vector xαβ in a stationary orthogonal reference frame can be depicted on a synchronous reference frame aligned to the rotor electric angle θ = θe , as shown in Fig. 7, through the rotation of the reference frame at the arbitrary angular velocity of the machine electrical frequency [8]: xdq = e− jθ xαβ ,

(11)

where xdq = xd + j xq is the vector representation of x in the dq axes. Therefore, the SMPMSM model in synchronous orthogonal reference frame is deduced by applying the transformation (11) to (9a): vm,dq = Rs im,dq + (L s − Ms )

dim,dq + ωe m Fdq . dt

(12)

Likewise, one can depict the electromagnetic torque developed by the machine as   Tel = n p m Fd i m,d + Fq i m,q .

(13)

Figure 8 shows the back-EMF waveforms on the synchronous reference frame. For the sinusoidal back-EMF waveform, both Fd and Fq are constants, with Fd = 0 √ and Fq = 3/2. Thus, the synchronous orthogonal reference frame is sufficient to

Fig. 7 Graphical representation of the synchronous orthogonal reference frame (dq) along with the stationary orthogonal reference frame (αβ) [7]

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Fig. 8 Back-EMF waveforms in synchronous reference frame

describe the SMPMSM model with sinusoidal back-EMF so that the electromagnetic torque is solely dependent on the quadrature component of the stator current:  Tel = n p m

3 i m,q . 2

(14)

However, for the SMPMSM with trapezoidal back-EMF waveform, Fd and Fq are not constants, so it is necessary to define a new reference frame that allows the torque modeling as directly proportional to one of the current components so that a constant current reference in such frame leads to a ripple-free electromagnetic torque.

2.3 The dqx Transformation for SMPMSM With Non-sinusoidal Back-EMF The dqx transformation is graphically presented in Fig. 9, and was proposed for non-sinusoidal PMSMs. Using this reference frame, it is possible to have a simple expression of the torque for PMSMs with any back-EMF waveforms [7].

Fig. 9 Graphical representation of the dqx reference frame along with the αβ and dq axes [7]

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Let a transformation be defined such that: xdqx =

1 − jθx e xdq , ax

(15)

where xdqx = xdx + j xqx is the vector representation of x in the dqx axes. By the appropriate choice of the terms ax and θx , the torque is directly proportional to the quadrature component of the stator current i m,qx . Therefore, the SMPMSM model in the dqx axes is deduced by applying the transformation (15) to (12):  vm,dqx = Rs im,dqx + (L s − Ms )

dim,dqx + ωe im,dqx dt + ωe m Fdqx ,



ax + j (1 + θx ) ax



(16)

where ax and θx are, respectively, the derivatives of ax and θx with respect to θe . The electromagnetic torque developed by the machine is described in this reference frame as:   (17) Tel = n p ax2 m Fdx i m,dx + Fqx i m,qx . The dqx transformation presupposes that the electromagnetic torque is directly proportional to the quadrature component of the stator current, so we can define, analogously to the sinusoidal back-EMF:

Fdx = 0

ax2 Fqx =



3 . 2

(18)

Based on (11) and (15), the solution for (18) is: ⎧  ⎨ax = 3 √ 1 2 Fα2 +Fβ2 ⎩θ = arctan −Fα − θ , x e Fβ and Fig. 10 shows ax and θx waveforms for an ideal trapezoidal waveform. Fig. 10 ax and θx as a function of θe for an ideal trapezoidal waveform

(19)

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Finally, it is possible to rewrite (16), splitting the direct and quadrature axis terms, as well as the electromagnetic torque in (17), considering the assumptions about Fdx and Fqx : 

vm,dx = Rs i m,dx vm,qx = Rs i m,qx

  di m,dx ax  + (L s − Ms ) i m,dx − (1 + θx )i m,qx + ωe dt ax    di m,qx ax  + (L s − Ms ) i m,qx + (1 + θx )i m,dx + ωe dt ax  3 1 m 2 ωe + 2 ax  Tel = n p m

3 i m,qx . 2

(20)

(21)

3 Vector Control for the SMPMSM As introduced earlier, choosing appropriate reference frames simplifies the modeling and control of the electrical machine: three-phase values can be expressed with complex vectors. The orientation of a vector reference frame for the design and control of a certain process is known as vector model-based control, or vector control for short [9]. Vector control techniques have an important role in AC machine drives nowadays, including SMPMSM applications. Advantages of vector control include good dynamic performance, increase in efficiency, and torque ripple reduction, among others [12]. Other techniques can also be used, such as scalar control and six-step with current control [12–14]. A common vector control structure is the field-oriented control, in which the quadrature axis current acts on the electromagnetic torque, and the direct axis current acts on the direct axis resultant magnetic flux. Since the rotor flux is defined by permanent magnets in SMPMSMs, it is usual to control the direct axis current at ∗ ∗ = 0 or i m,d = 0, in order to reduce the power losses, with the exception zero, i m,d x of machine operation in flux strengthening or weakening. Field-oriented control can be applied to SMPMSMs with sinusoidal back-EMF waveforms through the dq transformation, on a synchronous reference frame aligned to the rotor electric angle, as presented in Sect. 2.2. For SMPMSMs with nonsinusoidal back-EMF waveforms, the dqx transformation, introduced in Sect. 2.3, is the most suitable choice. It is possible to add the coupling and compensation terms to the output of the current controllers, shown in Table 1. This feed-forward strategy leads to less effort on the control. The coupling and compensation terms on the dq axes are a specific case of these terms on the dqx axes with ax = 1, ax = 0, and θx = 0. Thereby, Fig. 11 represents the final block diagram for the vector control of the SMPMSM in a wind generation system [7].

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Table 1 Coupling and compensation terms for the SMPMSM Terms dq axes (sinusoidal) Direct/quadrature decoupling Back-EMF compensation Non-sinusoidal back-EMF compensation

dqx axes (non-sinusoidal)

j (L s − Ms )ωe im,dq  j 23 m ωe

j (L s − Ms )ωe im,dqx (1 + θx )  j 23 m a12 ωe

–

(L s − Ms )ωe im,dqx

x

ax ax

Fig. 11 Block diagram for the vector control of the SMPMSM in a wind generation system. The terms of decoupling (blue), back-EMF compensation (red), and non-sinusoidal back-EMF compensation (yellow) are highlighted [7]. The abc/dqx transformation blocks use the terms ax and θx from Fig. 10

3.1 Control Methods for Current Control Figure 11 depicts the generic current controllers Cim,dx and Cim,qx . Several control methods can be used, both linear, such as Proportional-Integral (PI) [15] and Proportional-Integral-Derivative (PID) [12], and non-linear, such as sliding mode [16], model-predictive control [15] and fuzzy [17]. In this chapter, we chose the PI controller for both current loop controllers; thanks to its advantages, like robustness, zero steady-state error for type-0 systems, simple structure, easy digital implementation, and good transient performance, the PI controller is considered a consolidated technology [18–20].

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The structure of the PI controller is based on two components, the proportional (k P ) and the integral (k I ) parts. For a continuous-time implementation, they act directly on the error (t) = x ∗ − x(t) of the variable to be controlled x(t):  u(t) = k P (t) + k I

t

(τ )dτ + u(0),

(22)

0

where u(t) is the controller output control action and u(0) is the controller output initial value, usually zero. The discrete-time control law for the PI controller, assuming (0) = 0, is (23) u k = k P ( k −  k−1 ) + k I Ts  k + u k−1 , where Ts is the sampling time and the superscripts k and k − 1 refers to the current signal value and its previous value in the last time sampling, respectively. It was used the backward Euler method.

4 Rotor Position Estimation of SMPMSM The application of control techniques to the SMPMSM requires knowledge of the rotor position. It is common to use rotary position sensors (encoders) concomitantly with position estimation algorithms to increase the system’s reliability. Figure 12 presents a succinct classification of rotor position estimation methods for SMPMSMs [21–24]. For low-speed operation, including initial driving, saliencybased methods are used, like rotor fretting and pulse high-frequency signal injection. For medium and high-speed operation, methods based on mathematical modeling are applied. Non-adaptive methods use the back-EMF or flux linkage estimation to directly infer the rotor position, by means of zero-crossing, integration and thirdharmonic analysis. On the other hand, adaptive methods use closed-loop observation techniques for the back-EMF, the flux linkage or the rotor angle; those methods include Model Reference Adaptive System (MRAS), Artificial Intelligence (AI) estimators like artificial neural networks, and observer-based estimators, e.g. slidingmode observer, Luenberger observer and Kalman filter. Position estimation in SMPMSMs with non-sinusoidal waveforms (and, consequently, expressive harmonic content) involves a greater degree of complexity because the estimator may eventually synchronize with the higher order harmonic components of the back-EMF due to dynamic variations of the machine speed, causing the total desynchronization of the estimator [7]. However, wind generation systems work with a quasi-steady rotor speed operation. Furthermore, wind generation requires a medium to high-speed operation, so mathematical modeling methods are suitable for such an application. The big problem of the mathematical modeling non-adaptive methods is the uncertainty of the machine parameters; these are open-loop methods which do not guarantee a correct estimate. Therefore, the adaptive methods, also called closed-loop

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Fig. 12 Rotor position estimation methods for SMPMSM

methods, work by comparing the output signals of two different systems, a real system and a mathematically modeled system. The Kalman filter method is insensitive to measurement noise and parameter inaccuracy and has a good response when harmonics are present, therefore, it is a good alternative for wind generation with SMPMSM.

4.1 Kalman Filter-Based Approach for Rotor Position Estimation A Kalman filter-based estimator with two Phase-Locked Loops (PLLs) [25] is shown in Fig. 13. Each part of this method is described below: Kalman filter The Kalman filter estimates the SMPMSM back-EMF eαk , eβk based on k k k−1 the current measurements i m,α , i m,β , the applied voltages in the last time step vm,α , k−1 k−1 k−1 vm,β and the current measurements in the last time step i m,α , i m,β . The Kalman filter is preferred as a machine’s back-EMF estimator due to its rejection of sensor noise, producing an estimated back-EMF waveform closer to the real one [7]. Position estimator PLL This first PLL corrects the back-EMF angle θˆr from the Kalman filter by comparing it to a rotor angle θr from a back-EMF look-up table eα , eβ , culminating in a preliminary rotor angle estimation θˆrk .

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Fig. 13 Block diagram for rotor position estimation: Kalman filter (red); position estimator PLL (blue); and delay compensation PLL (yellow) [7]

Delay compensation PLL This PLL compensates the delay that is introduced by the Kalman filter, improving the control system dynamics. It uses the applied voltages in k−1 k−1 k−1 , vm,β and the current measurements in the last time step i m,α , the last time step vm,α   k−1 k k  k i m,β to estimate the currents i m,α , i m,β and their respective angle θi . The real current k k measurements i m,α , i m,β provide the actual angle θik . Then a rotor angle variation θrk is computed and added to θˆrk , resulting in a more accurate rotor position estimation θˆek . Note that ωˆ ek = d θˆek /dt ≈ (θˆek−1 − θˆek−2 )/Ts .

5 Grid-Side Converter Modeling and Control As introduced in Sect. 1, variable-speed-driven SMPMSMs applied to wind generation systems require full power converters. Since this chapter assumes the use of the back-to-back converter, it is possible to consider the grid connection modeling independently of the SMPMSM modeling.

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Fig. 14 Grid-side converter with an L filter [26]

The connection to the grid, with a two-level voltage source inverter, can be made through an L filter. The grid voltage is considered purely sinusoidal. Figure 14 depicts such a configuration, as well as the parameters and electrical quantities relevant to the modeling: i g,a , i g,b , i g,c are the grid phase currents; vg,a , vg,b , vg,c are the grid phase voltages; Vdc is the DC link voltage; L g and Rg are the L filter’s inductance and resistance, respectively; and Cdc is the DC link capacitance. It is considered that there is symmetry between the phases, and the magnetic and electrical properties of the used materials are linear. The electrical equation of the grid connection is: ⎡

⎤ ⎡ ⎤ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ vi,a i g,a vg,a vg,n i g,a d ⎣vi,b ⎦ = Rg ⎣i g,b ⎦ + L g ⎣i g,b ⎦ + ⎣vg,b ⎦ − ⎣vg,n ⎦ , dt i vi,c i g,c vg,c vg,n g,c

(24)

where vi,a , vi,b , vi,c are the inverter output voltages and vg,n is the voltage of the grid neutral terminal. The instantaneous active Pg and the reactive Q g powers delivered to the grid are denoted as: Pg = vg,a i g,a + vg,b i g,b + vg,c i g,c √ 3 (vg,b − vg,c )i g,a + (vg,c − vg,a )i g,b + (vg,a − vg,b )i g,c . Qg = 3

(25a) (25b)

The αβ and the dq transformations, as in (8) and (11), applied to (24) and (25), result in the grid model and the delivered active and reactive powers in terms of the grid voltages and currents in the synchronous orthogonal reference frame aligned to the a phase of the grid voltage:

Variable-Speed-Driven Three-Phase Surface-Mounted …

vi,dq = Rg ig,dq + L g

dig,dq + jωg ig,dq + v g,dq , dt

237

(26)

Pg = vg,d i g,d + vg,q i g,q

(27a)

Q g = vg,q i g,d − vg,d i g,q ,

(27b)

where ωg is the grid frequency. The angle of the grid voltage, θ = θg , is estimated by a quasi-type 1 PLL [27]. It is noteworthy that, for a purely sinusoidal grid voltage, vg,q = 0. Besides grid current, the DC link voltage shall be modeled. The nodal analysis in the DC link is: i P =i Cdc + i inv Pinv d Vdc + =Cdc , dt Vdc

(28)

where i P is the current from the rectifier (related to the generated power by the SMPMSM); i Cdc is the current flowing on the DC link capacitor; and i inv is the current entering the inverter. Neglecting the active power consumed by the L filter, one can denote Pinv ≈ Pg = vg,d i g,d . Furthermore, i P is assumed as a disturbance to the system to be controlled, so that the DC voltage dynamics can be described as: 2 d Vdc 2 + vg,d i g,d = 0. dt Cdc

(29)

Equation (29) shows a nonlinear relationship between Vdc and i g,d , while a linear relationship is desirable. To achieve such a relationship, we can use the small signals theory. The DC link voltage is described as a small perturbation signal vˆdc superimposed on the operating point V¯dc , so that Vdc = V¯dc + vˆdc . The intermediary steps are detailed in [28]. The resultant small signal model, neglecting high frequency terms and considering vg,d constant, and the process transfer function for the project of the controller is: vg,d ˆ d vˆdc + (30) i g,d = 0, dt Cdc V¯dc −vg,d Vdc (s) = . i g,d (s) V¯dc Cdc s

(31)

Figure 11 represents the block diagram for the vector control of the grid connection with a two-level voltage source inverter and an L filter, in a wind generation system. It is worth mentioning that the coupling and compensation terms, shown in Table 2, are added to the current controllers outputs in order to reduce the effort on the control.

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Table 2 Coupling and compensation terms for the grid control Terms Expressions Direct/quadrature decoupling Grid voltage compensation

jωg ig,dq v g,dq

Fig. 15 Block diagram for the vector control of the grid connection in a wind generation system. The terms of decoupling (blue), and grid voltage compensation (yellow), as well as the PLL structure (green), are highlighted

Current (Ci g,d and Ci g,q ) and DC voltage (C Vdc ) controllers can use the PI controller in its digital implementation, as presented in Sect. 3.1 (Fig. 15).

6 Example Case: 240 kW SMPMSM Generator As an example of the concepts presented in this chapter, a wind power generation system with an SMPMSM connected to the grid by a back-to-back converter will be studied through computer simulation. The generator parameters shown in Table 3 refer to a 240 kW commercial machine. The other simulation parameters, referring to the connection to the grid and the control, are also described in Table 3.

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Table 3 Simulation parameters Machine parameters Rated voltage Rated output power Rated frequency Synchronous speed Number of pole pairs n p Stator resistance Inductances subtraction L s − Ms Back-EMF normalized amplitude m Inertia coefficient Viscous friction coefficient Grid parameters Rated voltage Rated frequency Filter inductance L g Filter resistance Rg Rated DC voltage Vdc DC link capacitance Cdc Control parameters Sample time Ts Cim,dx , Cim,qx - Proportional gain Cim,dx , Cim,qx - Integral gain

380 V 240 kW 60 Hz 1250 rpm 3 0.0185  472 µH 0.853 Vs 4.76 kgm2a 0.1385 Nms/rada 13.8 kVb 60 Hz 400 µH 5 m 700 V 33,000 µF 20 µs 4.6 V/A 21 V/(As)

a Referenced b Through

to the machine rotor shaft a 440 V/13.8 kV 3φ transformer

6.1 Back-EMF Waveform Analysis Figure 16 presents the normalized back-EMF waveform of the 250 kW generator, and in Fig. 17, the real phase-a back-EMF waveform is compared to the ideal sinusoidal and trapezoidal waveforms. Note that the real waveform is similar to a sinusoid, although it has distortions. Since the back-EMF waveform is non-sinusoidal, we can use the dqx transformation. Figure 18 depicts ax and θx for the real back-EMF waveform of Fig. 16.

6.2 Simulation Tests Overview Two analyses are carried out concerning the SMPMSM vector control. Initially, a torque ripple analysis related to the choice of the back-EMF waveform in the SMPMSM vector control is performed. Three possibilities are considered:

240

Fig. 16 Real back-EMF waveform used in simulation

Fig. 17 Comparison between back-EMF waveforms Fig. 18 ax and θx as a function of θe for the real back-EMF waveform

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• the dqx transformation considering the real back-EMF waveform introduced in Fig. 16, which is the appropriate choice and is expected to present the best results regarding torque ripple; • the dq transformation, for which the control considers a purely sinusoidal backEMF waveform, allowing an interesting effect to be observed, since the real backEMF waveform resembles a sine wave; and • the dqx transformation considering an ideal trapezoidal waveform is presented for curiosity, as a way to show that the dqx transformation is the best solution only if the considered back-EMF waveform is the correct one. After this, the results with the real and with the sinusoidal back-EMF waveforms are compared in terms of the system efficiency. Mechanical parameters in Table 3 imply that speed variations occur over many seconds. Therefore, it is possible to consider that changes in speed are negligible when analyzing electrical variables in a time window of tens of milliseconds. The tests aforementioned are performed at a mechanical speed of 1000 rpm and with a torque reference of −1900 Nm.1

6.3 Simulation Results Simulation results of electromagnetic torque, phase a back-EMF, and phase currents for the adopted machine considering the three variations cited in Sect. 6.2, are presented. Figure 19 shows the results for a vector control in the dqx axes adopting the real back-EMF waveform; Fig. 20 refers to the vector control in the dq axes, therefore the phase currents are sinusoidal; and Fig. 21 presents the results for a vector control in the dqx axes adopting a ideal trapezoidal signal for the back-EMF waveform. The use of the dqx transformation with the real back-EMF waveform reduced the torque ripple. This becomes clear when computing the torque ripple factor, shown graphically in Fig. 22: using the dqx transformation with the real back-EMF waveform reduced the ripple factor by 16.5% compared to using the sinusoidal back-EMF and by 61.6% compared to the ideal trapezoidal back-EMF. The torque ripple factor r T e is defined by:  2 2 Te,r ms − Te,mean , (32) rT e = Te,mean where Te,r ms is the root mean square value of the electromagnetic torque and Te,mean is its mean value. The results reiterate that considering a back-EMF different from the real one in the control leads to unsatisfactory behavior, with a higher torque ripple. Clearly, Fig. 20 presents the sixth torque ripple harmonic component due to the real 1

Considering a positive rotor speed, a negative electromagnetic torque means the power is converted from the mechanical to the electrical system.

242 Fig. 19 Electromagnetic torque (Tel ), phase a back-EMF (ea ) and phase currents (i m,a , i m,b and i m,c ) considering the real back-EMF waveform in the SMPMSM vector control

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Tel Tel∗

-1000Nm -1500Nm 1900Nm

200V 100V ea 0 -100V -200 500A 250A iabc 0 -250A -500A 0.1

0.105

0.11

0.115

0.12

0.125

0.13

0.135

0.14

t(s)

back-EMF being non-sinusoidal. Furthermore, the use of the trapezoidal waveform, Fig. 21, is illustrative in this aspect, bringing undesirable results. For the efficiency analysis, the real and the sinusoidal back-EMF waveforms are considered in the control. Table 4 presents the system input and output powers, and highlights that the global efficiency is similar when considering a sine wave or the real back-EMF waveform in the vector control.

Variable-Speed-Driven Three-Phase Surface-Mounted … Fig. 20 Electromagnetic torque (Tel ), phase a back-EMF (ea ) and phase currents (i m,a , i m,b and i m,c ) considering a sinusoidal back-EMF waveform in the SMPMSM vector control

0

243

Tel Tel∗

-1000Nm -1500Nm 1900Nm

200V 100V ea 0 -100V -200 500A 250A iabc 0 -250A -500A 0.1

0.105

0.11

0.115

0.12

0.125

0.13

0.135

0.14

t(s)

7 Chapter Outcomes and Summary This chapter presented the modeling and vector model-based control of SMPMSMs considering both sinusoidal and non-sinusoidal back-EMF waveforms. Rotor position estimation and the modeling and control of the grid connection were also examined. Simulation results with parameters from a real wind power generation system evince that the dqx transformation shows itself as an appropriate choice for SMPMSMs with non-sinusoidal back-EMF for torque ripple factor reduction. Since torque ripple reduces the life of the electrical machine and all the elements mechanically coupled to it, such a reduction is a desirable feature.

244 Fig. 21 Electromagnetic torque (Tel ), phase a back-EMF (ea ) and phase currents (i m,a , i m,b and i m,c ) considering a trapezoidal back-EMF waveform in the SMPMSM vector control

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Tel Tel∗

-1000Nm -1500Nm 1900Nm

200V 100V ea 0 -100V -200 500A 250A iabc 0 -250A -500A 0.1

0.105

0.11

0.115

0.12 t(s)

Fig. 22 Torque ripple factor (r T e ) varying the considered back-EMF waveform in the SMPMSM vector control

0.125

0.13

0.135

0.14

Variable-Speed-Driven Three-Phase Surface-Mounted … Table 4 System power and efficiency Considered back-EMF Real Electromagnetic torque Input: mechanical power Injected power to the grid Efficiency

1908.84 Nm 199.893 kW 189.320 kW 94.71%

245

Sinusoidal 1906.71 Nm 199.670 kW 189.070 kW 94.69%

Acknowledgements Authors would like to thank the University of São Paulo (USP) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Finance Code 001 for the financial support.

References 1. Kakran S, Chanana S (2018) Smart operations of smart grids integrated with distributed generation: a review. Renew. Sustain. Energy Rev. 81:524–535 2. Vineetha CP, Babu CA (2014) Smart grid challenges, issues and solutions. In: 2014 international conference on intelligent green building and smart grid (IGBSG), pp 1–4 3. Roy P et al (2022) Recent advances of wind-solar hybrid renewable energy systems for power generation: a review. IEEE Open J Ind Electron Soc 3:81–104 4. Dalwadi P et al (2011) Optimization of solar-wind hybrid system for distributed generation. In: 2011 Nirma university international conference on engineering, pp 1–4 5. European Commission et al (2018) JRC wind energy status report: market, technology and regulatory aspects of wind energy: 2016 edition. Publications Office 6. Milivojevic N et al (2008) Electrical machines and power electronic drives for wind turbine applications. In: 2008 34th annual conference of ieee industrial electronics. IEEE, pp 2326– 2331 7. Monteiro J (2017) Acionamentos Elétricos com Máquinas Síncronas com Ímã Permanente no Rotor. Technical report habilitation thesis. University of Sao Paulo, Sao Carlos, p 126 8. Krause P et al (2013) Analysis of electric machinery and drive systems. IEEE press series on power engineering. Wiley. isbn: 9781118524268 9. Quang N, Dittrich J (2015) Vector control of three-phase ac machines: system development in the practice, 2nd edn. Springer, Berlin 10. Duesterhoeft W, Schulz M, Clarke E (1951) Determination of instantaneous currents and voltages by means of alpha, beta, and zero components. Trans Am Inst Electr Eng 70(2):248–1255 11. Grenier D, Louis J (1995) Modeling for control of non-sinewave permanentmagnet synchronous drives by extending Park’s transformation. Math Comput Simul 38(4):445–452 12. Biyani V et al (2021) Comparative study of different control strategies in permanent magnet synchronous motor drives. In: 2021 IEEE 5th international conference on condition assessment techniques in electrical systems (CATCON), pp 275–281 13. Kwon Y, Kim S, Sul S (2014) Six-step operation of PMSM with instantaneous current control. IEEE Trans Ind Appl 50(4):2614–2625 14. Cho B et al (2021) Simple position sensorless V/f scalar control method for permanent-magnet synchronous motor drives. J. Power Electron 21(1):1020–1029 15. Zhang Z et al (2019) Advanced control strategies for back-to-back power converter PMSG wind turbine systems. In: 2019 ieee international symposium on predictive control of electrical drives and power electronics (PRECEDE), pp 1–6

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16. Chatri C, Ouassaid M, Labbadi M (2021) A novel nonlinear sliding mode control scheme for PMSG based on wind energy conversion system. In: 2021 3rd international conference on control systems, mathematical modeling, automation and energy efficiency (SUMMA), pp 1108–1112 17. Gencer A (2017) Modelling of operation PMSG based on fuzzy logic control under different load conditions. In: 2017 10th international symposium on advanced topics in electrical engineering (ATEE), pp 736–739 18. Nise N (2013) Control systems engineering, 7th edn. Wiley, Hoboken, NJ 19. Fu X et al (2020) A strongly robust and easy-tuned current controller for PMSM considering parameters variation. IEEE Access 8:44228–44238 20. Hussain H (2021) Tuning and performance evaluation of 2DOF PI current controllers for PMSM drives. IEEE Trans Transp Electrif 7(3):1401–1414 21. Tarmizi Y et al (2017) Review and comparison of sensorless techniques to estimate the position and speed of PMSM. Int J Power Electron Drive Syst 8(3):1062–1069 22. Li Q et al (2018) Sensorless control for surface mounted PM machine with a high inertial load. CES Trans Electr Mach Syst 2(1):116–122 23. Li Y et al (2021) Rotor position estimation approaches for sensorless control of permanent magnet traction motor in electric vehicles: a review. World Electr Veh J 12(1) 24. Singh S, Tiwari A (2017) Various techniques of sensorless speed control of PMSM: a review. In: 2017 2nd international conference on electrical, computer and communication technologies (ICECCT), pp 1–6 25. Almeida T et al (2017) Sensorless vector control for BLDC machine. In: 2017 Brazilian power electronics conference (COBEP), pp 1–6 26. Santos S (2021) Operação de motor de indução trifásico conectado á rede por meio de um conversor de nove chaves com fator de poténcia adiantado. MA thesis. São Carlos: Escola de Engenharia de São Carlos, Universidade de São Paulo 27. Golestan S et al (2014) A quasi-type-1 phase-locked loop structure. IEEE Trans Power Electron 29(12):6264–6270. https://doi.org/10.1109/TPEL.2014.2329917 28. Zarif M, Monfared M (2015) Step-by-step design and tuning of VOC control loops for grid connected rectifiers. Int J Electr Power Energy Syst 64:708–713

Variable Speed Drives for Household Wind Energy Systems: Model Predictive Control of the Squirrel Cage Induction Generator with the Nine-Switch Converter Paulo Roberto Ubaldo Guazzelli, Stefan Thiago Cury Alves dos Santos, and Manoel Luís de Aguiar

Abstract Household power generation is a key part of smart grids. Among the power sources, variable speed wind turbines offer good power output with reliability. This chapter investigates the use of a squirrel cage induction generator in a variable speed drive system for the aforementioned application. In this downsized scale of power, reduced-count switch converters are interesting alternatives, for the sake of cost and size reduction. Therefore, this work applies the Nine-Switch Converter (NSC) in the integration of a squirrel cage induction generator onto the grid, with use of Finite Control Set Model Predictive Control (FCS-MPC), a powerful control theory, specially for power electronics. The converter is analyzed, with focus on its available voltage vectors, leading to the development of two structures of FCS-MPC: the concentrated approach and the decoupled approach. Furthermore, the decoupled approach enables the incorporation of duty cycle optimization into FCS-MPC for the NSC, which leads to improvements on the generator torque ripple and on the grid active power ripple. Additionally, considerations are made about the use of NSC wind energy system also for grid reactive power compensation. All the considerations are accompanied by simulated results of the considered system. As a result, the NSC is a feasible alternative for variable speed drive wind energy systems in household applications.

P. R. U. Guazzelli (B) Department of Electrical Engineering, Federal University of São Carlos, Rodovia Washington Luis, km 235, São Carlos, SP, Brazil e-mail: [email protected] S. T. C. A. dos Santos · M. L. de Aguiar São Carlos School of Engineering, Avenida Trabalhador São-carlense, 400, São Carlos, SP, Brazil e-mail: [email protected] M. L. de Aguiar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_9

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1 Introduction This chapter embraces and summarizes the results and advances about the NineSwitch Converter (NSC) made under the supervision and guidance of Prof. Dr. Manoel Luís de Aguiar at University of São Paulo. Mainly, the Ph.D. thesis of Dr. Paulo Roberto Ubaldo Guazzelli [60], about the decoupled Finite Control Set Model Predictive Control (FCS-MPC), and the duty cycle optimization for FCS-MPC in the NSC. The reactive power compensation derives from the master dissertation of M.Sc. Stefan Thiago Cury Alves dos Santos [61]. Scientific publications derived from these works are found in [1–4].

1.1 Smart Grids and Household Wind Power Generation The smart grid concept enables efficient power management between diverse power sources and loads, using state-of-the-art technologies for renewable energy, signal processing, power electronics, and communication systems [5, 6]. Its implementation can improve the power system operation and provide benefits for consumers [7, 8]. Among the smart grids challenges, the renewable and distributed generation deployment and integration is a topic of great interest [9]: low-voltage and lowpower distributed energy resources such as photovoltaic modules [10, 11], biomass systems [12], and wind turbines [13, 14] are increasingly present in both residential and industrial contexts. All these technology gaps and challenges lead to intense efforts from researchers. This chapter focuses on the latter research topic, nominally the deployment of low-voltage and low-power wind turbines for household applications. Household applications for wind power generation have become possible with the decrease in its installation and maintenance costs. The Squirrel-Cage Induction Generator (SCIG) is an appropriate choice because of the simplicity and lower maintenance of this electric machine compared to other ones like the doubly-fed induction generator or the permanent magnet synchronous generator [15, 16]. Although the SCIG requires a fully-rated converter to operate with a variable speed turbine, the costs of the power electronics devices have diminished over the years, and ReducedSwitch-Count AC/AC Converters (RSCs) can lower costs and complexity by the decrease of the number of electronic switches [17]. Figure 1 presents the diagram of a variable speed turbine with a SCIG and a fully-rated converter.

1.2 Nine-Switch Converter Fully-rated converters applied to electricity generation using AC machines demand that both AC terminals shall be controllable; on one hand, the machine-side must be

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Fig. 1 Diagram of a variable speed turbine with a fully-rated converter

able to handle a variable-speed operation, and on the other hand, the grid-side needs the capabilities of electric power delivery and power factor control [18, 62]. Figure 1 depicts the three-phase AC/AC converter as a set of two controlled converters, a rectifier one (AC/DC converter) and an inverter one (DC/AC converter), and they are connected through a DC link. This configuration is known as the Back-to-Back Converter (B2B) and needs 12 controlled power switches for two-level converters. As said earlier, RSCs aim to decrease the number of semiconductor devices of a AC/AC converter, compared to B2B, maintaining its capabilities. Examples of RSCs are the Eight-Switch Back-to-Back converter [19], the Six-Switch Converter [17] and the Nine-Switch Converter (NSC) [20]. The NSC, Fig. 2, can be understood as two converters that share three power switches S2 , S5 and S8 among the rectifier and the inverter parts. As a result, this converter has 25% less switches than B2B. NSC stands out from other RSCs for not having derivations on the DC link. Such derivations can deteriorate system performance [21], so avoiding them is an advantage for the NSC.

Fig. 2 Nine-switch converter (NSC)

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1.3 Finite Control Set Model Predictive Control The application of power electronics in power generation systems requires highperformance control techniques. Direct Torque Control (DTC) [22] and Field Oriented Control (FOC) [23] are widely used in the control of three-phase induction machines; on the grid-side, Direct Power Control (DPC) [24] and Voltage Oriented Control (VOC) [25] are usual options. These techniques adopt control methods such as PID, sliding mode, deadbeat, and fuzzy, among others, in their current control loops [26–29]. The Finite Control Set Model Predictive Control (FCS-MPC) [30] is another high-performance control technique that has gained relevance in recent years. This technique considers the discrete nature of power converters to determine each future state of the system from all possible voltages applied to the converter terminals; in this way, the optimization of the control signal through the minimization of a cost function is solved by the usage of a search algorithm [31]. Among the advantages of FCS-MPC, one can cite the easy inclusion of nonlinearities and secondary control objectives and the fast transient performance [32]. There are several types of FCS-MPC structures. When the cost function considers current errors, we have the Predictive Current Control (PCC); when torque and flux errors are considered, Predictive Torque Control (PTC); when active and reactive power errors are considered, Predictive Power Control (PPC) [33]. PCC is analogous to FOC and VOC in its current control in a specific reference frame, PTC is analogous to DTC in its torque and flux control perspective, and PPC is analogous to DPC in its power control perspective. One of the FCS-MPC characteristics is the variable switching frequency, limited to half of the adopted control frequency, due to the absence of modulators [33]. This feature simplifies the converter implementation but can lead to greater ripple in the controlled variables. The inclusion of the duty cycle concept in FCS-MPC significantly improves steady-state performance, at the cost of using modulators. As a consequence, there is a fixed switching frequency [34]. FCS-MPC has been successfully used in the NSC. The most common approach in the literature is to use a single cost function that concentrates the control objectives of the two converter ports. This approach was used in applications with loads connected to both three-phase terminals of the NSC [35–37] and with one of the terminals connected to the grid, in a configuration analogous to B2B [4, 38]. NSC with FCSMPC was also used as a power quality conditioner [39]. However, condensing the control objectives into a single cost function implies more computational burden due to the high number of search possibilities (up to 27) [40] and may favor the control of one of the ports to the detriment of the other [41]. The separation of the cost function into two parts, which are evaluated in different control periods, mitigates the aforementioned difficulties [2, 40]. Furthermore, duty cycle optimization becomes possible for such an approach [41].

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1.4 Reactive Power Compensation Certain loads, such as electrical machines directly connected to the grid, cause electrical power quality problems related to low power factor. Since those problems can lead to increased expenses due to lower efficiency and even penalties in several countries [42, 43], consumers may benefit from the application of power factor correction techniques. A conventional technique for lagging power factor correction is the addition of capacitor banks. However, it is not possible to control the amount of reactive power supplied [44], and capacitor banks are sensitive to harmonic components [45]. Solutions that use power converters in order to exclusively supply reactive power can overcome the existing difficulties in the conventional technique and have been extensively studied in recent years. Static Var compensators and static synchronous compensators are examples [46–48]. Another possible approach is to use the inherent capability of power factor control present in the B2B converter and its RSC counterparts [49–51]. Such an approach was successfully proposed for an NSC-fed electric motor drive with both fixed and variable-frequency control schemes on the machine-side [3, 4, 52].

1.5 Chapter Objectives and Outline Given the previous comments and literature review, this chapter develops the theoretical concepts regarding NSC application in household wind generation with SCIG, with corresponding simulation results for validation. Four PCC techniques are discussed and compared based on simulation results: the Concentrated FCS-MPC; the Reduced FCS-MPC; the Decoupled FCS-MPC; and the Duty Cycle Based FCSMPC. The capability to supply reactive power by the NSC during active power generation is also studied. After the contextualization of Sects. 1 and 2 presents the necessary fundamentals for the components of the generation system, whilst Sect. 3 delves into the system control, with a deeper analysis of the different FCS-MPC implementations for the NSC control in Sect. 4. Simulated results are presented in Sect. 5 for validation, and Sect. 6 draws the conclusions.

2 System Modelling FCS-MPC requires the knowledge of the system models. As a result, the SCIG and grid analytical models are presented, as well as the enumeration of the NSC possible Voltage Vectors (VVs).

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2.1 Squirrel Cage Induction Generator The used SCIG model is oriented in the rotor flux angle θ2 synchronous reference frame [53]. As a result, the rectifier port current ir is represented by (1), where the symbol j is the complex operator: ir = i dr + ji qr = e jθ2 (i αr + ji βr ).

(1)

The resulting model is given by (2), for a voltage v1 applied to the stator winding of the SCIG:    R L2 v1 R1 L H  R2 dir 2 H = i − + + jω + − j pω 2 r m ψ2 dt σ L1 σ L1 σ L1 L2 L2 σ L 1 L 22 R2 L H dψ 2 R2 = ir − ψ2 dt L2 L2

(2a) (2b)

where the stator and rotor resistances are given by R1 and R2 , respectively; the stator, rotor and mutual inductances are given by L 1 , L 2 and L H , respectively; σ := L2 1 − L 1 HL 2 is an auxiliary variable; and the number of pole pairs is represented by p. Also, the mechanical speed is represented by ωm , and ω2 is the angular speed of the rotor flux ψ 2 . Since the rotor flux is aligned to its own reference frame, its direct value is its own amplitude, and its quadrature value is zero. Therefore, the rotor flux amplitude and angle are estimated according to the recursive structure of (3): R2 L H R2 dψ2d = i dr − ψ2d dt L2 L2 R2 L H i qr ω2 = pωm + L 2 ψ2d  θ2 = ω2 dt.

(3a) (3b) (3c)

2.2 Three-Phase Grid Let θg be the angle of the grid voltage v g . The inverter port currents ii can be aligned to the grid voltage reference frame, with direct and quadrature coordinates: ii = i di + ji qi = e jθg (i αi + ji βi ).

(4)

The grid model is given by (5), and represents the connection between the NSC and the grid by the means of an L filter, with inductance L f and resistance R f :

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 1  dii = vi − v g − R f ii − jωg ii . dt Lf

253

(5)

In this work, the angle θg was estimated by a synchronous reference frame PLL with Moving Average Filter (MAF-PLL). This structure features good performance under unbalanced voltage conditions [54], and is consolidated in three-phase applications [55].

2.3 Nine-Switch Converter Each leg from NSC has three switches. In order to avoid floating phases on any ports, there must be always two switches turned on and one switch turned off. This renders 27 different switching combinations for the NSC, which are listed on Table 1. However, each port can assume a more reduced set of possibilities: only eight VVs, whose coordinates are listed in Table 2. It can be seen how the VV applied to rectifier port is a function only of the switching states of the three upper switches [S1 , S4 , S7 ], and that the VV applied to the inverter port is dependant only of the switching states of the lower switches [S3 , S6 , S9 ]. There are two null VVs and six active VVs, which are organized in an hexagonal shape, as in Fig. 3. Each active VV has half of the amplitude of the corresponding VV from a regular six-switch inverter. Therefore, the relation between the DC bus voltage of the NSC and maximum phase voltage at the AC ports is given by (6). √ r ms . vdc = 2 6v phase

(6)

3 System Control Figure 4 shows the overall control structure. Grid and SCIG currents are measured and converted to their respective synchronous reference frames. On the SCIG side, ∗ is set by a PI controller responsible for the rotor flux the direct current reference i dr ∗ is direct calculated from amplitude control, whilst the quadrature current reference i qr ∗ the torque reference, according to (7). On the grid side, the direct current reference i di is set by a PI controller responsible for the DC bus voltage control, and the quadrature ∗ is set according to the reactive power reference Q ∗ , in (8). current reference i qr ∗ = i qr

1 2L 2 · Tel∗ = · Tel∗ KT 3 pL H ψˆ 2d ∗ i qi =−

2Q ∗ . 3vdg

(7)

(8)

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Table 1 Voltage vectors for each port according to the switching states of NSC #

Switching state Applied vector [S1−9 ] Upper port Lower port

1 2 3 4 5 6 7 8 9 10 11 12 13

[110110110] [110110011] [110110101] [110011110] [110011011] [110011101] [110101110] [110101011] [110101101] [011110110] [011110011] [011110101] [011011110]

#

Switching state Applied vectors [S1−9 ] Upper port Lower port

14 15 16 17 18 19 20 21 22 23 24 25 26 27

[011011011] [011011101] [011101110] [011101011] [011101101] [101110110] [101110011] [101110101] [101011110] [101011011] [101011101] [101101110] [101101011] [101101101]

v7 v2 v7 v6 v1 v6 v7 v2 v7 v4 v3 v4 v5

v0 v5 v4 v3 v4 v7 v2 v7 v6 v1 v6 v7 v2 v7

v7 v2 v2 v6 v1 v1 v6 v1 v1 v4 v3 v3 v5

v0 v0 v5 v0 v0 v4 v3 v3 v5 v0 v0 v5 v0 v0

The FCS-MPC block is the core of the control system, determining the control action based on the currents measurements and references. Firstly, the two FCS-MPC core tasks must be addressed: prediction and cost function calculations.

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Table 2 Voltage vectors coordinates on the αβ reference frame, as a function of the DC bus voltage, for the rectifier port (left) and inverter port (right) switching combinations # Vector vα vβ Amplitude [S1 , S4 , S7 ] [S3 , S6 , S9 ] 0

v0

0

1

v1

2 3 vdc

2

v2

1 3 vdc

− 13 vdc

4

v4

− 23 vdc

6

v6

− 13 vdc

7

v7

1 3 vdc

0

[0,0,0]

[1,1,1]

0

2 3 vdc

[1,0,0]

[0,1,1]

2 3 vdc

[1,1,0]

[0,0,1]

2 3 vdc

[0,1,0]

[1,0,1]

2 3 vdc

[0,1,1]

[1,0,0]

2 3 vdc

[0,0,1]

[1,1,0]

2 3 vdc

[1,0,1]

[0,1,0]

0

[1,1,1]

[0,0,0]

3 3 vdc

v3

v5

0

√

3

5

0

√

3 3 vdc

0 − − 0

√

3 3 vdc

√

3 3 vdc

Fig. 3 NSC voltage vectors in the αβ reference frame

3.1 Grid Side PCC The prediction stage for the grid control at a k instant for the nth VV is given by (9): iik+1 = iik +

 tD  vn − vkg − R f · iik − jωg L f iik Lf

(9)

where t D is the discretization time. The cost function is then calculated as: 2  2  k+1 k+1 ∗ ∗ ˆ ginvn = i di − iˆdi + i − i . qi qi n n

(10)

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Fig. 4 Diagram of the system: grid and SCIG connected to the NSC, with PCC control

3.2 SCIG Side PCC SCIG control requires the prediction of the dq currents at the rectifier port, according to (11): 

    R L2 L H  R2 vn R1 2 H k k k k ˆ = + tD − j pωm ψ2d + − + + jω2 ir σ L1 L2 L2 σ L1 σ L1 σ L 1 L 22 (11) with the corresponding cost function calculation: irk+1

irk

2  2  k+1 ∗ ∗ k+1 ˆ − iˆdr + i − i . gr ectn = i dr qr qrn n

(12)

4 FCS-MPC Implementation The aforementioned PCCs, for the grid and the SCIG ports, need to result in the final control action: the switching states for each one of NSC nine switches.

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Fig. 5 Control flowchart of PCC-C

4.1 Concentrated FCS-MPC The first FCS-MPC implementation for the NSC found in the literature is the herein named Predictive Current Control with a Concentrated cost function and a set of 27 pairs of vectors (PCC-C). It treats the NSC control as one single control. This is achieved by merging the two PCCs into a single cost function, given by (13). This cost function depends on both the SCIG and the grid PCCs, according to the flowchart of Fig. 5. At each time step, it evaluates the cost for each one of the 27 pairs of VVs found in Table 1. The resulting VV corresponds to an optimal switching state for the nine switches, according to its respective row in Table 1. gn = gr ectn + ginvn .

(13)

4.2 Reduced FCS-MPC The evaluation of 27 pairs of VVs is a costly task for embedded systems. Looking into Table 1, these pairs can be classified into four main types: • • • •

Group 1 (null-null VVs): #1, #14 and #27 pairs; Group 2 (null-active VVs):#3, #7, #9, #19, #21, and #25 pairs; Group 3 (active-null VVs):#15, #17, #18, #23, #24, and #26 pairs; Group 4 (active-active VVs): #2, #4, #5, #6, #8, #10, #11, #12, #13, #16, #20, and #22 pairs;

Group 4 of VVs is redundant, since the other types already contain all the VVs for every port. Also, the three null-null pairs are redundant. Therefore, one can only evaluate one VV from group 1, and all the VVs from groups 2 and 3. The resulting PCC is herein named Predictive Current Control with a Concentrated cost function and Reduced set of 13 pairs of vectors (PCC-CR). Figure 6 depicts its structure, which is analog to the one from PCC-C, but with a smaller enumeration of VVs. The chosen VVs are depicted in Table 3.

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Fig. 6 Control flowchart of PCC-CR Table 3 Reduced set of voltage vectors for PCC-CR # Switching state Applied Vectors [S1−9 ] Upper Port 1 2 3 4 5 6 7 8 9 10 11 12 13

[110110110] [110110101] [110101110] [110101101] [011011101] [011101011] [011101101] [101110110] [101110101] [101011011] [101011101] [101101110] [101101011]

v7 v7 v7 v7 v5 v3 v4 v7 v7 v1 v6 v7 v2

Lower Port v7 v2 v6 v1 v0 v0 v0 v4 v3 v0 v0 v5 v0

4.3 Decoupled FCS-MPC However, the grid and SCIG controls are initially unrelated: therefore, it is desirable to keep them apart. As a result the Predictive Current Control with Decoupled cost functions (PCC-D1) was developed. In PCC-D1, the two PCC structures are never merged into a single cost function. Instead, each time step evaluates only one of the controls (grid or SCIG), according to Fig. 7. The structure for each PCC is seen in Fig. 8. In an even time step, all the NSC lower switches are turned on. This implies there is a null VV applied to the grid port, and the SCIG PCC must only determine the switching states for the three upper switches. There are two reductions in the computational burden of the technique. Firstly, the size of the enumeration: now only the seven VVs from Table 2 need to be considered. Secondly, the evaluation of

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Fig. 7 Overall control flowchart of a decoupled PCC

Fig. 8 Control flowcharts of PCC-D1: SCIG (a) and grid (b) controls

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both PCC at the same time step is no longer necessary: in an odd time step, the three upper switches are turned, and therefore there is a null VV applied to the SCIG port. As a result, only the grid PCC is evaluated, for only seven VVs, and it determines the switching states of the lower switches, according to the respective row of from Table 2. Noteworthy in Fig. 8 is that the value of the VVs must be adjusted, dividing it by half. This is due to the time in which the null VV is being applied to the port.

4.4 Duty Cycle Based FCS-MPC The last improvement mentioned here for the NSC control is the incorporation of duty cycle optimization for the PCCs. The Predictive Current Control with a Decoupled cost function and Duty cycle optimization (PCC-D2) determines not only the optimal VV for each port, but also its respective duration time. The remaining time is fulfilled by a null VV, thus modulating the amplitude of the chosen VV.

4.4.1

Torque Ripple Minimization opt

Associated with the optimal VV for the upper/rectifier port, there is a dwell time tr . In order to reduce the torque ripple of the system, we will actuate on the quadrature opt current i qr . We need to find the value tr which takes the system faster as possible ∗ to the i qr reference, according to the deadbeat principle: k+1 k ∗ = i qr + rn tropt + r0 (t D − tropt ) → i qr i qr

(14)

where rn and r0 are the derivative terms of the plant due to the n-th VV and due to the null VV, respectively, and are calculated by: r0 = −

R1 L 22 + R2 L 2H L H pωm ψˆ 2d i qr − ω2 i dr − σ L1 L2 σ L 1 L 22 rn = r0 + opt

vq n . σ L1

(15)

(16) opt

As a result, the optimal time tr and the optimal duty cycle δr below: opt ∗ k i qr − i qr − r0 t D tr . δropt = = tD t D (rn − r0 )

are calculated as (17)

Noteworthy that the SCIG prediction stages must be updated with its respective dwell time, as below:

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 irk+1 = irk + t D

261

opt    L H  R2 δr vn  R2 L 2H R1 k k k ˆ − j pωm ψ2d + − + + jω2 irk . σ L1 L2 L2 σ L1 σ L1 σ L 1 L 22

(18)

opt

The relation between the optimal time δr the switches Sl for l ε{1, 4, 7} is given by:

and the input for the modulation of

m l = δropt Sl . 4.4.2

(19)

Active Power Ripple Minimization

PCC-D2 follows the same approach for the grid control, however, it acts on the direct current i di , which is related to the active power injected into the grid. Therefore, the opt duty cycle δi is given by: opt

δi

=

k ∗ − i di − i0 t D i di (in − i0 )t D

(20)

where the grid respective derivative terms are calculated as: i0 =

−vd g − R f i di + ωg i qi Lf in = i0 +

vd n . Lf

(21)

(22) opt

The grid prediction stage for PCC-D2 incorporates δi , as follows: iik+1 = iik +

 t D  opt δi vn − vkg − R f · iik − jωg L f iik . Lf

(23)

Finally, the input for the modulation of the lower switches, which means Sl for l ε{3, 6, 9} is calculated as: opt (24) m l = (1 − δi )Sl . Therefore, PCC-D2 follows the structures of Fig. 9. It can be seen the addition of the optimization stages in the loops, which increases the computational burden of the technique. On the other hand, the loop is reduced from seven to six VVs, since the null VV is already implicit in the evaluation.

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Fig. 9 Control flowcharts of PCC-D2: SCIG (a) and grid (b) controls

5 Simulated Results Computational simulation allows to verify system behaviors before the physical implementation, which saves time and resources [56]. All simulations were obtained in MATLAB® /Simulink, from MathWorks® , with the ode45 solver, for the four PCC strategies presented in the past section. There is no dead time consideration. The parameters used for the simulation are depicted in Table 4, from Appendix. The simulations use the parameters from a real 73/127 V SCIG, obtained according the procedure of [57]. The DC bus voltage reference was set to 357 V, according to the SCIG rated voltage and the calculation of (6). Additionally, the outer loops have PI controllers with the anti-windup method of integrator saturation [58], for the rotor flux amplitude and the DC bus voltage, with their parameters also shown in Table 4. Noteworthy here that, since the four techniques were simulated with the same acquisition and measurement frequency (20 kHz), consequently PCC-D1 and PCCD2 had only half of the control frequency of PCC-C and PCC-CR. The former techniques only evaluate each port control every other step, alternatively, whilst the latter techniques evaluate the port controls at every step.

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The following tests are considered: • Steady-state test: under 7 Nm, with steady-state speed of 1600 rpm; • Dynamic test: applied torque step from 3 to 6 Nm, with speed at 1500 rpm; • Dynamic test: reactive power step from 0 to 400 VAr, with speed at 1200 rpm, under applied torque of 2 Nm; • Performance indexes: SCIG torque ripple factor, grid active power ripple factor and grid current THD for steady-state speeds ranging form 800 rpm to 1600 rpm, under torque of 7 Nm.

5.1 Steady-State Analysis Figure 10 shows the SCIG speed, torque, current and rotor flux for the steady-state test at 1600 rpm, with a time window of 0.15 s. From the speed waveforms, it is clear the machine is operating in generator mode, with a slip of −4.2%. It can be seen in Fig. 10c how PCC-D1 reduced the torque ripple when compared to PCC-C and PCC-CR, with only half of the control frequency and a smaller set of voltage vectors. PCC-D2 further reduced the torque ripple, as shown in Fig. 10d. This is in accordance to the performed optimization. The analysis of the d and q current graphs show that the quadrature current i q reached the value of 12.5 A in order to provide the desired torque of 7 Nm, and presented a high ripple for the three techniques without duty cycle optimization, PCC-C, PCC-CR e PCC-D1. This is a trait from the conventional FCS-MPC [59]. The optimization performed by PCC-D2 managed to significantly reduce that oscillation, which prompted the corresponding reduction on the torque ripple. Finally, the rotor flux reference of 0.21 Wb was reached for all cases, with a corresponding direct current i d of 7.1 A. For the same test, Fig. 11 shows the grid waveforms. The quadrature grid current was set to zero, in order to keep the reactive power to zero. On the other hand, a direct grid current of 9.40 A is injected into the grid in order to maintain the DC bus voltage at 357 V. This corresponds to a power injection of 840 W. It can be seen how PCC-D1 and PCC-D2 has direct currents with a lower ripple than PCC-C and PCC-CR. PCC-D2 has the smaller ripple between all techniques, as a consequence of the duty cycle calculation. This ripple reduction directly affects the active power ripple reduction also.

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Fig. 10 Simulated results of SCIG speed, torque, currents and rotor flux at steady-state speed of 1600 rpm, under applied torque of 7 Nm

5.2 Torque Step Change Figure 12 has the waveforms of SCIG speed, torque currents and rotor flux for the simulated torque step change. For all techniques, the system reached the new reference set. Quadrature and direct current controls remained decoupled, without interference on the rotor flux amplitude. After the step, the quadrature current value increase from 5.4 to 10.8 A. Figure 13 shows a zoomed version of some variables from Fig. 12, for a better view of the dynamics. The four techniques presented a fast dynamics, with settling times under 1 ms. PCC-C and PCC-CR presented settling times of 0.15 ms, and PCC-D1 and PCC-D2 had settling times of 0.30 ms. Figure 14 shows the effects of the torque step change on the grid side variables. Given the presence of a PI controller in the DC bus voltage control loop, these variables have a slower response. The torque step change implied a larger power

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Fig. 11 Simulated results of DC bus voltage and grid currents and power at steady-state speed of 1600 rpm, under applied torque of 7 Nm

input, and therefore a rise on the DC bus voltage. It presented an overshoot, which was corrected by an increase in the direct current value from 3.9 to 7.7 A; it corresponded to an active power increase from 350 to 690 W. There was no effect on the reactive power an in the quadrature grid current, which remained set to zero. Again, it is clear how PCC-D2 presented the performance with smaller ripples.

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Fig. 12 Simulated results of SCIG speed, torque, currents and rotor flux for a torque step change from 3 to 6 Nm, at 1500 rpm Fig. 13 Simulated results of SCIG speed, torque, currents and rotor flux for a torque step change from 3 to 6 Nm, at 1500 rpm—detailed comparison of the four techniques

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Fig. 14 Simulated results of DC bus voltage and grid currents and power for a torque step change from 3 to 6 Nm, at 1500 rpm

5.3 Reactive Power Step Change The SCIG waveforms of speed, torque, currents and rotor flux for the dynamic test of reactive power step are seen in Fig. 15. The SCIG variables did not suffered influence from the reactive power step change, for all techniques, showcasing how the performance of one port is not affected by the other one. Figure 16 shows a different scenario on the grid variables. The new reactive power reference implied in a increase in the grid quadrature current reference, from 0 to 4.6 A. As a result, the system begun inserting the desired reactive power into the grid, without interference on the system active power injection of 150 W. Figure 17 has a zoomed time window of some variables from Fig. 16.

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Fig. 15 Simulated results of SCIG speed, torque, currents and rotor flux for a reactive power step change from 0 to 400 VAr, at 1200 rpm

5.4 Performance Indexes Figure 18 shows the SCIG torque ripple factors, the grid active power ripple factors, and the grid current THDs for each technique, for seven speed ranging from 800 to 1600 rpm. The ripple factors were calculated according (25), and the THD of the current considered the 50 first harmonic components, according (26). fr (x) = 100%

R M S(x AC ) R M S(x DC )

THD(i) = 100%

50 2 n=1 In

I1

(25)

(26)

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Fig. 16 Simulated results of DC bus voltage and grid currents and power for a reactive power step change from 0 to 400 VAr, at 1200 rpm Fig. 17 Simulated results of DC bus voltage and grid currents and power for a reactive power step change from 0 to 400 VAr, at 1200 rpm—detailed comparison of the four techniques

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Fig. 18 Performance Indexes—SCIG Torque Ripple Factor (a), grid active power ripple factor (b) and grid current THD (c) for a range of simulations at different SCIG speeds under applied torque of 7 Nm

PCC-CR generally depicted larger ripple factors than PCC-C, as well as larger THDs. Therefore, the reduction on the universe of voltage vectors did impacted on the performance. The decoupling of the cost functions in PCC-D1 impacted with generally lower ripple factors than PCC-CR, but with generally larger THDs. The addition of duty cycle optimization in PCC-D2 managed to improve the overall performance, with ripple factors consistently lower than the other three techniques, by a far margin. On the other hand, the current THD was generally reduced when compared to PCC-CR and PCC-D1, but still surpassed by PCC-C.

6 Conclusions This work evaluated the use of FCS-MPC in a variable speed drive comprised of a SCIG and a NSC, a reduced switch count converter, which is an interesting choice for small-size systems, such as the ones for household applications usually found in smart grid systems. After the definition of the prediction and cost functions stages of FCS-MPC, special attention was devoted to the implementation of the enumeration

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voltage vectors, and also to the system steady-state performance. As a result, four different PCCs were presented, with improvements in the VV enumeration and in the addition of duty cycle optimization. Simulated results showed how the improvements of FCS-MPC reduced the torque and active power ripple factors of the system. Additionally, PCC-D1 and PCC-D2 provided a control structure which results in smaller computational burdens in embedded applications, mitigating an FCS-MPC known drawback. Also, the system showed itself feasible in a wide array of situations, from steady-state condition to dynamic test of torque chance, and also of reactive power compensation. Therefore, this work stimulates the application of FCS-MPC and of the NSC to household wind energy systems, in smart grid systems. Acknowledgements Authors would like to thank the Pró-Reitoria de Pesquisa da USP, the Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—under Finance Code 001, for the financial support.

Appendix See Table 4.

Table 4 System and control parameters for the simulation results Parameter

Value

Unit

Parameter

Value

Unit

Rated torque

6.1

Nm

Pre-processing (low-pass filter)

660

Hz

Rated voltage (/Y)

73/127

V

Measurement frequency

20

kHz

Rated speed

1750

rpm

Outer control frequency (PCC-C and PCC-CR)

20

kHz

Rated rotor flux

0.21

V·s

Outer control frequency (PCC-D1 and PCC-D2)

10

kHz

∗ ψ2d

0.21

V·s

Inner control frequency

1

kHz

R1

0.8088



Cdc

750

μF

R2

0.2648



Lf

8.5

mH

L1

33.1

mH

Rf

0.167



L2

33.1

mH

∗ vdc

357

V

LH

29.5

mH

Grid line voltage

73

V

p

2

–

Grid frequency

60

Hz

K P (ψ2d ) K I (ψ2d )

40

–

K P (vdc )

0.05

–

400

–

K I (vdc )

1.25

–

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Electromagnetic Analysis of a DFIG‘s Controlled Operation Using Finite Elements Method André L. L. F. Murari, J. S. Solís-Chaves, Ademir Pelizari, Alfeu J. Sguarezi Filho, Bruno H. P. da Silva, and Renato M. Monaro

Abstract The Doubly Fed Induction Generator (DFIG) is widely employed in wind energy and this type of source is interesting to the smart grids environment. In this way, this chapter proposes an electromagnetic analysis using the finite element method for the DFIG based Wind Energy System, during its vector controlled operation by means of a proportional-integral (PI) controller. Moreover, a new design method for the PI controller gains obtention is presented, based on the fact there is no guarantee that the DFIG will operate in the unsaturated condition. Therefore, it can compromise the performance of the power control strategy. Simulation and experimental results endorse the analyzes during DFIG normal operating conditions.

1 Introduction In order to meet the ever-growing demand for electricity in the current world context, it is necessary to invest in power generation methods using renewable primary sources with maximum possible efficiency. Today, the highlight is to produce electricity from the force of the winds (wind power), for it is the fastest growing in the world. The commonly used machines for converting wind energy into electrical are mainly synchronous and induction generators. Since wind speed is not constant, A. L. L. F. Murari · A. Pelizari · A. J. S. Filho Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André, SP, Brazil e-mail: [email protected] A. Pelizari e-mail: [email protected] J. S. Solís-Chaves (B) Mechatronic Engineering Department, ECCI University, Bogotá, DC, Colombia e-mail: [email protected] B. H. P. da Silva · R. M. Monaro Polytechnic School, University of São Paulo (USP), São Paulo, SP, Brazil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_10

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wind generation systems must operate at variable speed using power electronics devices have been adopted. The doubly-fed induction generator (DFIG) working in variable speeds systems is an efficient technology due to its accurate control and the low cost of back-to-back (partial scale) converter, compared with fixed speed induction squirrel cage generators and variable speed generators operating with full-scale converters [1, 2]. Vector control is the most widely used technique to operate DFIG wind turbine systems. It is based on stator-flux or stator-voltage orientation [3, 4]. This technique allows controlling active and reactive power independently by regulating rotor current. Some researchers investigate the use of PI controllers and stator-flux orientation [5–7]. The PI gains design was studied in electrical machine drives using Bode diagram as presented in [8]. Regarding the PI gains design of DFIG, Marques [9] applied the eigenvalues to reach this objective with interesting results. However, the choice of the model can increase its complexity. Works as [10, 11] apply the pole compensation technique to design the PI gains in power and current loops control for DFIG, respectively. Another possibility is using the Modulus Optimum and the Symmetrical Optimum as is shown by [12]. The model used herein has some simplifications who can degrade the performance of the controllers. Advanced controllers for DFIG-based Wind Energy Systems cover a range from mathematically complex techniques such as model-based predictive control, including several control strategies as GPC [13], Repetitive Control, etc. to Artificial Intelligence techniques such as fuzzy control [14], neural networks and optimization methods such as PSO [15]. An interesting compilation of most relevant and popular MPBC DFIG control techniques are presented in [16] including predictive control techniques with modulator and finite control sets with experimental results. Regarding the DFIG presence in some micro-grids/smart-grids scenarios based in renewable energy systems are studied in works as [17, 18]. In this, a frequency control of a DFIG working in a micro-grid is proposed using a modification to the MPPT strategy must be made. These modified wind turbines have a new Grid Side Converter (GSC) that governs DC voltage control in the Back-to-Back capacitor, as is also shown in [18]. In DFIG power control, the machine operates below its rated values. Hence, the applied voltages and currents are limited under their rated values, which means that the ferromagnetic material does not operate over-saturated. However, there is no guarantee that the machine will operate in the unsaturated condition in which compromise the performance of the power control. Hence, the finite element methods can be a powerful tool for DFIG analysis during the controlled operation [19–21]. In this chapter, a design for the PI controller gains by applying the symmetrical optimum methods for DFIG power control is proposed. In this, the power control is achieved by rotor current control, as there is no guarantee that the DFIG will operate in the unsaturated condition during the power control. The analysis of the DFIG conditions in terms of magnetic induction, using computational simulations by the FEM has been also proposed, in order to verify and to estimate the DFIG induction levels and thus ensure that the controller operation ensure the machine non-saturation

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condition. These are the contributions of this work. Also, experimental results from a setup during the controlled operation, endorse the proposed PI design method.

2 Wind Generation Outlook Wind generation is leading the transition from fossil fuels to clean energy production. Top 12 displayed in the pie diagram in Fig. 1 illustrates the consolidation of North American and European markets. The installed global wind power on shore capacity is around 707.37 GW at the end of 2019, a growth of 4.5% compared to the end of 2019 (620.96 GW ). The off shore total installed capacity is 35.29 M W at the end of 2020 [22]. This is shown in Fig. 2. A Brazilian Wind Energy panorama is also presented in Sect. 2.2.

2.1 Worldwide Wind Generation Of all WWG capacity, just 6.06 GW of new onshore wind turbines capacity was installed in 2021, a decrease of 2% compared to 2020 (with 6.24 GW). Moreover, Fig. 1 shown the unstoppable expansion of Asian markets, with India as a major example of this fast growth up with a total of 38.6 GW at the end of 2020 (an increase of 1 GW compared to 2019). Latin American markets are growing faster, with Brazil as a good example with 17.7 GW , meaning a 3% of the global cumulative capacity. The onshore market for this region is promising and shown a growth in countries as Mexico, Chile and Argentina, with 6,7, 2,6 and 2.8 GW , respectively. The offshore wind turbines market falls by 0.5%, from 6.24 GW in 2019 of new wind farm installations to 6.06 GW in 2020. In Fig. 2 can be shown the Total of WG offshore installations at December 2020 [22]. China is in the first position with 37% of the world total, including 211392 M W of total installations. The total installed onshore wind grew by 9%, whilst total offshore wind grew by 20%, reaching 23 GW. GWEC forecasts [22] that offshore wind will become an increasingly global market. If governments remain committed, and projects and investments continue, annual installations in Asia could reach 5 GW or more each year. In the USA, GWEC expects the developing offshore wind market to reach 1 GW by 2023.

2.2 Brazilian Wind Generation Brazil has more than 468 wind power projects in operation from 2017 and at the end of the 2022, totaling 752 wind turbines accounting for more than three quarters of the

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Fig. 1 Total on shore installations [MW]—Dec. 2020

Fig. 2 Total off shore installations [MW]—Dec. 2020

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Fig. 3 Wind energy in Brazil—2020. Adapted from [24]

installations in South America, with 12332.15 M W . Figure 3 shows the generated wind power in Brazil. States in white color do not have wind energy generation. States as Rio Grande do Norte, Bahía Ceará and Rio Grande do Sul, are in the top of the installed Brazilian capacity [23]. Brazilian Wind generation reached 424 T W h, which represents an increase of 26.5%, wind power is around 12.763 M W with an expansion of 21.3% at the end of 2017 [23]. Precisely, the North–eastern Region which most contributes to this type of generation with 6.75 GW , the South Region has a 1.78 GW of power generated and in South–eastern Region the power generated just becomes 28.2 M W [24–26]. In fact, 98 projects are currently working in that Brazilian region. Ceará state has the biggest Wind Turbine with a rated power of 105 M W named Praia Formosa and Paraíba has the smallest unit (Milennium) with a rated power of 10.2 M W . The wind energy produced in the North–Eastern Region accounts for 77% of total energy generated in this way in Brazil in the same period [25–27].

3 Machine Model and Rotor Current Vector Control Doubly fed induction generator equations considering synchronous reference frame (dq) can be expressed by [28, 29], as follows:

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Vdqs = Rs i dqs +

dψdqs + jωs ψdqs dt

(1)

ωsli p

Vdqs = Rs i dqr

  dψdqr  + + j (ωs − P Pm ) ψdqr dt

(2)

a relationship between fluxes and currents is: ψdqs = L s i dqs + L m i dqr

(3)

ψdqr = L m i dqs + L r i dqr

(4)

and generator active (Ps ) and reactive (Q s ) power are: Ps =

3 (vds i ds + vqs i qs ) 2

(5)

Qs =

3 (vqs i ds − vds i qs ) 2

(6)

Subscripts s and r represent the stator and rotor parameters respectively, ωs is the synchronous speed, ωm is the mechanical speed, ωr is the angular frequency of voltage and currents of the rotor windings, Rs and Rr are the stator and rotor windings electrical resistance (per phase), L s , L r and L m are the proper and mutual inductances of the stator and rotor windings, v , i and ψ are the voltage, current and flux space vectors respectively, P P is the machine number of pair of poles. In this paper, mechanical speed m is an input of the DFIG. The main characteristic of the DFIG power vector control is the stator active (Ps ) and reactive (Q s ) power control independence by the rotor current regulation. For this purpose, Ps and Q s are expressed as functions of each rotor current, i dr and i qr . Using the stator flux-oriented control, which decouples the dq reference frame ψds = ψs = |ψdqs |and ψqs = 0 means, (3) becomes: i ds =

ψs Lm − i dr Ls Ls

i qs = −

Lm i qr Ls

(7)

(8)

In the same way, using stator flux-oriented the stator voltage becomes vds = 0 and vqs = vs = |Vdqs |. Hence, the Ps (5) and Q s (6) power can be computed by using (7) and (8): 3 Lm i qr (9) Ps = − vs 2 Ls

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Fig. 4 DFIG control overview

Qs =

3 vs 2



ψs Lm − i dr Ls Ls

 (10)

Thus, it is possible to control the DFIG active and reactive powers by controlling the rotor currents. In order to regulate the rotor currents, proportional integral controllers (PI) can be used as is presented in Fig. 4.

4 Converter Control Tuning The rotor circuit differential equations (2), (3) and (4) can be rewritten as (11) and (12): L m dψds di dr + (11) vdr = Rr i dr − ωr σ L r i qr + σ L r dt L s dt vqr = Rr i qr − ωr σ L r i dr + σ L r where σ = 1 −

di qr dψds + ωr dt dt

(12)

L 2m . Ls Lrs

In order to facilitate the control design (11) and (12) can be simplified to (13) and (14) considering dψdtds = 0 (utility voltage constant) and treating (Lm/Ls)ψd s as perturbation [29].

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vdr = Rr i dr − ωr σ L r i qr + σ L r

di dr dt

(13)

vqr = Rr i qr − ωr σ L r i dr + σ L r

di qr dt

(14)

Note the cross-coupling terms −ωr σ L r i qr and −ωr σ L r i dr between the two axis, its decoupling can be obtained by defining vdr and vqr as is explained in [30]: vdr = vdr + ωr σ L r i qr

(15)

vqr = vqr − ωr σ L r i dr

(16)

Applying the Laplace transformation in (13) and (14), i dr and i qr respond to vdr and vqr respectively, using a first-order transfer function Hr (s), Hr (s) =

i qr i dr 1/Rr = = vdr vqr 1 + τr s

(17)

where τr = σRLr r . The PWM generator is taken into account by Hd (s), a first-order transfer function [11], vout 1 (18) Hd (s) = = vctrl 1 + τα s where vctrl and vout are the input control and output voltages, respectively. τα is a time delay related to control computation and PWM generation, for the sake of simplicity, it is taken as the inverse of the PWM carrier frequency, τα = 1/ f pwm . Given a controller G c (s), a decoupled control is obtained by the feedback loop: ref

ref

vdr = G c (s)(i dr − i dr ) − ωr σ L r i qr

(19)

ref ref = G c (s)(i qr − i qr ) − ωr σ L r i dr vqr

(20)

where the superscript ref represents the reference values. Figure 5 shows the block diagram for i dr and i qr currents control considering the transfer functions afore-presented. The open loop transfer function is, HO Lr (s) = Hd (s)Hr (s) =

1/Rr (1 + τα s)(1 + τr s)

(21)

as τα >> τr then 1+τ1 r s can be approximated to τ1r s [19], which implies that HO Lr (s) can be simplified to: 1/Rr τr appr ox (22) HO Lr (s) = (1 + τα s)s

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Fig. 5 Current control diagram block

Using (22), the symmetrical optimum method can be applied to tune a PI controller. This method was chosen for its several advantages such as phase margin maximization, good perturbation rejection and simple and well-defined tuning relations [31]. The PI parameters (23) can be calculated with (24) [32]. G c (s) = K p

1 + Ti s Ti s

 Rr τr 2 Ti , K p = a Tα αTα

(23)

(24)

A lower value of a leads to faster response but worse damped [30].

5 Performance Evaluation of Different Adjustment Methods Considering a 3 kW 380 V DFIM, the parameters of which are available in Table 7, the current PI controllers were designed according to the method described in Sect. 2. In order to evaluate the proposed theoretical method adjustment, simulation and experimental analyses were performed. Only the performance analysis was performed in the response of the controller seeing that the DFIG control operation is well documented in the literature [29] and, also, because the magnetic analysis is the other objective of this paper.

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Table 1 PI current loop settings Setting α 1 2 3

kp

2 ωc Tα 1 ωc Tα

–

ki

57.08

2.82 × 104

114.16

2.25 × 105

3.63

626.44

Fig. 6 Theoretical step response

Table 1 summarizes three design configurations applied, of which the first two were based on the optimum symmetric method by varying parameter α, and the last one based on the method proposed by [33], which discusses the PI adjustment using the pole compensation method. As a design criterion, a response time of 10 ms was defined in order to obtain the controller gains, described in equations (25) and (26). kp = ki =

1 σ Lr 5 × 10−3

(25)

1 Rr 5 × 10−3

(26)

In Table 1, ωc refers to the open-loop crossover frequency considered as one order of magnitude smaller than the PWM switching frequency to avoid noise [30]. Figure 6 shows the theoretical step response considering the control block diagram shown in Fig. 5. Figure 6 allows observing that setting #2 has the fastest time response but with high overshoot. The first adjustment has lower rise time but faster damping. The third configuration showed the slowest rise-time but it does not presented overshoot. The performance indicators of each configuration are presented in Table 2; the settling time is computed considering a 2% error band. To further evaluate the PI controllers adjustment an electromagnetic transient simulation of a DFIG machine, the rotor side converter and its rotor current control were performed with the Matlab/Simulink software.

Electromagnetic Analysis of a DFIG‘s … Table 2 Control performance indicators Setting Rise time #1 #2 #3 * 2% error band

0.18 [ms] 0.48 [ms] 10.87 [ms]

287

Settling time* (%) 1.34[ms] 3.81 [ms] 19.41[ms]

Overshoot 20.51 6.48 0.0

Fig. 7 Simulation step response for i qr

The DFIG parameters are available in Appendix A in Table 7. A time step of 1 µs was used. The DFIG stator was connected directly to an 380 VL L , r ms, 60 Hz ideal three-phase voltage source. The DC capacitor and the grid side converter was replaced by an ideal 680 VDC voltage source. A two level voltage source converter (VSC) operating with 100 kHz PWM frequency was used to supply the DFIG rotor windings. Figure 7 shows the i qr step response simulation considering the three PI controller settings. For this test the mechanical speed was set to 90% of synchronous angular frequency and i dr was set to zero. The rise-times for setting #1 and #2 are similar to the values observed in the theoretical analysis. On the other hand setting #3 presented a smaller rise-time than expected. Considering the overshoot, setting #1 shows a small increase in comparison to the theoretical analysis; setting #2 presents a small reduction for the expected value and setting #3 displays a significant increase (from 0% to approx 21%). It is worth noting that no limiters were employed in the controllers simulation. Differences between the theoretical and simulation analysis results can be attributed to simplifications used to obtain simplified decoupled transfer functions. Also, the response of i dr in which i qr was set to 1 A, is presented in Fig. 8 and it has nearly the same behavior. In order to validate the simulation results and the PI gains design an experimental setup was used. The proposed PI gains were implemented in a real DFIG with the same characteristics used for the theoretical and simulation analyses. The DFIG is driven by a 3 kW Direct Current Motor (DCM). The controller was embedded in

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Fig. 8 Simulation step response for i dr

Fig. 9 Experimental setup

a digital signal processor (DSP) Texas Instruments TMS320F335, using electronic boards for signal conditioning, and an encoder with 3600 pulses per revolution. The space vector modulation used a 10 kHz switching frequency. The DFIG parameters are presented in Appendix A in Table 7. This bench is shown in Fig. 9. For each controller design evaluated a set of step response analysis was performed. Figure 10 shows one test in which the reference for i qr was changed from −1 A to −3 A at t = 0 s and i dr was adjusted to 1 A. Also, Fig. 11 shows another test in which the reference for i dr was changed from 1 A to 3 A at t = 0 s and and i qr was adjusted to 2 A. For these tests the DFIG operate at sub-synchronous speed with 10% slip. The rise-time and overshoot for the three PI design are similar to the values obtained from the simulation analysis. Small differences between simulation and experimental results can be explained by the signal condition delay, signal acquisition errors and controller limiters (required for safe operation) not considered in the simulation stage. Based on the theoretical, simulation and experimental analysis, setting #1 had a better performance, small overshoot and good setting-time. It also presented consistent response for the three analyses performed. The results show that the optimum symmetric method

Electromagnetic Analysis of a DFIG‘s …

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Fig. 10 Experimental step response for i qr

Fig. 11 Experimental step response for i dr

considering the parameter α = ωc1Tα with Tα = 1/ f pwm and ωc an order smaller that the switching frequency is a good design rule to determine PI gains applied to DFIG rotor current control.

6 Finite Element Method Simulation The main goal of the finite element method simulation is to estimate under a specific value of slip the flux density level in the ferromagnetic material of the induction generator. Based on the stator and rotor flux densities levels, it is possible to check some potential saturation that can to hinder the operation of the control system behavior. Inasmuch as the machine operate in a DFIG arrangement, the injection of currents in the rotor is required by a power converter to maintain the correct frequency and power to the grid. Thus, two simulations were made using two different values of current in the rotor. At a first simulation, a current of 3.16 A was injected into to the slip rings. During the second simulation, a value of 2.23 A was used. In both situations, a rotor current frequency of 6 H z was adopted according to the condition of the slip. These values of current and frequency were obtained during the laboratory tests in the prototype.

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Table 3 DFIG prototype data DFIG general data Type Number of phases Rated power Poles Rated speed

WRIG 3 3,0 [kW] 4 1700 [rpm]

Table 4 DFIG Stator data Stator data Number of slots Winding Coil pitch Rated stator voltage (Y) Rated rotor voltage (Y) Rated rotor current

36 Double layer 12(slots) 220 [V] 380 [V] 11,5 [A]

Source Author’s own Table 5 DFIG rotor data Rotor data Type Number of slots Winding Coil pitch

Slip rings 27 Double layer 7 (slots)

Source Author’s own

Tables 3, 4 and 5 show the data of the prototype installed in the LEPS (Laboratory of Renewable Energy and Power Electronics) at the Universidade Federal do ABC (UFABC).

7 Magnetic Transient Simulation To evaluate the DFIG controller operation a magnetic transient regime was applied. The purpose of the magnetic transient regime simulation is to evaluate the flux density levels in the ferromagnetic material of the DFIG. The problem formulation with constant speed, according to the Equation (27), is [34];  × v × A = J − σx

∂A − σx V +  × HC + σx ω ×  × A ∂t

(27)

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Table 6 DFIG simulation data Simulation data Scalar electric potential (V) Operation speed (v) Grid frequency Rotor frequency(s = 60.0,10003184) Simulation time Time step

220[V] 1619,4 [rpm] 60 [Hz] 6 [Hz] 0,02 [s] 0,0002 [s]

Were the constants v, σx and ω are magnetic reluctivity, electric conductivity and the speed in steady state, respectively. In the same equation, V is the source scalar electric potential; HC is the permanent magnet coercive force, which is zero in this case, seeing that there are not permanent magnets; vector J is the current density of the conductors and A is the potential vector. Table 6 shows the quantities used in this simulation. Due the limited memory size of the computer as well as the symmetry of the problem, it is much more convenient to use half of the geometry. Figure 12 represents the detailed geometry used and in Fig. 13 the boundary conditions of A vector are shown. The ferromagnetic material used during the simulation step was the 1010 carbon steel in which the B H magnetization curve is presented in Fig. 14. Figures 15 and 16 represent the results from the computational simulations. The color maps show the levels of flux density in the stator and in the rotor of the induction generator according to the conditions described in the experimental results section.

Fig. 12 Detail of the DFIG geometry: a Stator and rotor. b Winding stator. c Winding rotor

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Fig. 13 Specific boundaries, physical properties and moving band for magnetic transient analysis for DFIG

Fig. 14 DFIG BH curve of the 1010 carbon steel used in the stator and in the rotor

8 Conclusions This chapter proposed a design for the gains in the PI controller for a field-oriented control of DFIG by using the symmetrical optimum methods. Also, magnetic analyses during control operation are performed. The correct selection of the gains will impact the system response with a null steady-state error. The simulation and experimental results endorse the proposal during the DFIG operation and present several desired responses. Hence, the magnetic analysis was performed to guarantee that the DFIG will operate in the unsaturated condition during the control operation. The simulation results, in all parts of the geometry, show that the flux densities obtained were under the sat-

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Fig. 15 Flux density color map for a RMS current of 2,23 [A] in the DFIG rotor

Fig. 16 Flux density color map for a RMS current of 3,16 [A] in the rotor

uration point of the ferromagnetic material used which is about 2.1 T ; consequently, the control system can operate correctly, avoiding perturbation during the operation without saturation.

Appendix A: Doubly-Fed Induction Generator Parameters See Table 7.

294 Table 7 Data used in the tests

A. L. L. F. Murari et al. Nominal values Stator active power Per phase voltage Stator Frequency Pair of poles number Parameters Stator resistance Rotor resistance Magnetizing inductance Stator inductance Rotor inductance Moment of inertia

PN VN f PP Rs Rr Lm Ls Lr J

3.5 [kW] 220 [V] 60 [Hz] 2 1 [] 3.13 [] 192 [m H ] 200 [m H ] 200 [m H ] 0.45 [kg × m 2 ]

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Signal Processing and Communication Systems

Signal Processing Technologies Applied in Smart Grids Guilherme Torres de Alencar, Ricardo Caneloi dos Santos, and Aline Neves

Abstract Reliable protection schemes for modern electrical energy systems play a vital role for minimizing the consequences of fault occurrences. Taking advantage of the processing and communication power of intelligent electronic devices, this chapter presents innovative solutions based on different signal processing techniques for fault detection, classification and location in power systems. The presented methods are based on Euclidean Distance and Independent Components Analysis, proving that more accurate and fast solutions can be reached when using the resources available in modern power grids, implementing smart grids concepts. The presented methods were tested against a system with real characteristics and then compared with conventional methods.

1 Introduction Electrical Energy Systems (EES) have become more complex mainly due to the high penetration of distributed generation (with and without converters) and also the significant increase of power demand over the last few decades. As a result of this new scenario, EES have been operating closer to their limits, imposing challenges to system operators [1]. Moreover, conventional protection schemes might fail against this new EES operating condition, as these schemes were designed for more conservative scenarios before the mentioned changes in the power sector. Therefore, due to the continuous changes, suitable protection schemes able to quickly detect and classify faults and so accurately locate them, is still an issue to be addressed [2]. G. T. de Alencar · R. C. dos Santos (B) · A. Neves Center of Engineering, Modeling and Applied Social Science, Federal University of ABC, Santo André, SP, Brazil e-mail: [email protected] G. T. de Alencar e-mail: [email protected] A. Neves e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_11

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Fig. 1 IED application in modern EES

Despite the challenges posed by new EES, they were followed by notable technology advances in the Intelligent Electronic Devices (IED) used to monitor, control and protect power systems. Nowadays, modern IED have high processing and communication capabilities, allowing the development and implementation of sophisticated methods for fault detection, classification and location, based on advanced signal processing techniques and wide communication networks for information exchange [3]. Figure 1 illustrates this concept, often referred to as Smart Grid, where IED exchange information among them and with the control room to monitor and protect an EES with different types of distributed generation [4]. Due to the unquestionable importance of this topic for quickly recovering the EES in case of fault, a large number of fault detection, classification and location methods have been proposed by experts working in this area [5–11]. However, some of them are very complex and therefore unfeasible in practical terms, as the trade-off between performance and computational burden is not favorable. On the other hand some of them are relatively simple, but with limitations considering their scope of application, still having an unfavorable trade-off. Taking advantage of modern IED with processing and communication resources, this work proposes a new method for fault detection and classification, based on Euclidean Distance to measure similarity of voltage signals. The main characteristic of the proposed method is its simplicity and accuracy, allowing its implementation for practical application [12]. Moreover, for

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the sake of completeness, this work also presents an accurate fault location method, based on Independent Components Analysis (ICA). It is important to highlight that these proposals are possible due to the remarkable advances in items such as IED and communication networks available in modern EES. Both methods are evaluated against a real power grid operating in different conditions, while one of them is compared with a traditional method for fault location. The results show promising performance and significant benefits, when considering the possibilities offered by modern EES planned with intelligent devices and reliable communication channels.

2 Foundations In this section we will review some classical methods used for signal processing, that will be important for the understanding of the developments and discussions on the rest of the chapter. Emphasis was given on the most important aspects of each method. Further information and details may be found in the references.

2.1 Blind Source Separation Problem Blind source separation (BSS) is a problem that has called the attention of researchers since 1950 when Cherry studied the recognition of a message from sound mixtures [13]. However, only in the 80’s the problem begun to receive more attention, when Hérault, Jutten and Ans [14] tried to computationally separate muscle signals in order to distinguish between displacement and angular speed of a movement. A classical example of the BSS problem is the cocktail party problem. In a party, we are able to speak with others and understand what they are saying, despite the noise caused by other conversations and music. However, if the sound of the room is recorded by several microphones, the problem of isolating the voice of a desired subject is not so simple. That is exactly what BSS techniques search to achieve. Examples of where such techniques may be applied sweeps a large variety of applications: communication problems [15], audio, voice and image processing [16, 17], biomedical signals [18], geophysical data processing [19], as well as fault location in EES [20]. Retrieving sources from a mixture without any a priori knowledge about the signals or how they were mixed is a very complex task and still does not have a solution [21, 22]. Thus, a few assumptions, that are usually satisfied by measured signals in practical situations, are necessary. The most accepted and used is that of considering the original sources as being independent from each other. Such assumption gives rise to the well known ICA [23]. In the following, we briefly present the mathematical modeling of such problem. Let us consider a linear mixing process. Each source is represented by a signal si (n) given by a stochastic process of zero mean. Mixtures can thus be written as:

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Fig. 2 Blind source separation system model

x(n) = A s(n),

(1)

where x(n) = [x1 (n) x2 (n) . . . x M (n)]T is the mixture vector, the source vector is s(n) = [s1 (n) s2 (n) . . . s N (n)]T and A is the mixture matrix, upper-script T denotes transposition. Usually, N = M. Since s(n) and A are unknown, the objective is to recover the sources using the measured signals from x(n). Thus, we must find a matrix W, ideally the inverse of A, such that y(n) = W x(n), (2) where y(n) = [y1 (n) y2 (n) . . . y N (n)]T are the estimated sources. A scale and order ambiguity are possible [23]. The system model is presented in Fig. 2. To estimate W, the independence among sources is explored. As shown in [24], if the sources are non-Gaussian (or only one source has a Gaussian distribution), it is possible to separate the sources, using x(n), restoring the independence between the estimated signals. Thus, the two most used cost functions in ICA are mutual information defined as [23]: I (Y ) =

N 

H (yi ) − H (y),

(3)

i=1

where H (·) is the entropy, and negentropy, defined as a measure of non-gaussianity JN (y) = H (ygauss ) − H (y),

(4)

where ygauss is a Gaussian vector of the same covariance matrix as y. Negentropy explores the fact that, as stated by the Central Limit Theorem, mixtures tend to be more Gaussian than the original signals themselves. Since the sources are nonGaussian signals (or at most one is Gaussian), the objective is to search for signals that are as non-Gaussian as possible (maximizing negentropy). Mutual information is always positive, being equal to zero only if the signals are independent. Negentropy is also always nonnegative, being equal to zero if y is Gaussian. Such cost functions lead to the most used and known algorithms in the context of ICA: FastICA and InfoMax [22, 23]. It is important to notice that, before applying such algorithms, the mixture signal x must be whitened:

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z = V x,

303

(5)

where V is a matrix obtained from the eigendecomposition of the covariance matrix of x. After (5), E[zzT ] = I , with I being the identity matrix. Another approach that is largely used in BSS problems is exploring the secondorder statistics of the signals involved in the process, leading to simple and efficient algorithms. This class of methods can be applied when sources present different power spectral densities, i.e., E[si (n)si (n − τ )] = E[s j (n)s j (n − τ )],

(6)

with i = j and τ = 0. Differently from ICA, algorithms in this class explore the temporal structure (correlation among samples) of the signals and depend only on second-order statistics. Considering that the data has been whitened through (5), the autocorrelation matrix of the observed mixtures for a delay τ can be written as Rz (τ ) = E[zzT ] = QRs (τ )QT

(7)

where Rs (τ ) is the autocorrelation matrix of the sources s(n). Since s(n) are considered to be independent (or are at least decorrelated), Rs (τ ) will be a diagonal matrix, indicating that (7) is the eigendecomposition of Rz (τ ). Thus, we may conclude that the eigendecomposition of Rz (τ ) will result in the rotation matrix that enables the recovery of the sources. It suffices to make W = Q in (2). Such method corresponds to the AMUSE (Algorithm for Multiple Unknown Signals Extraction) [25]. It is interesting to note that such algorithm can separate more than one Gaussian source, as long as the signal presents a temporal structure. AMUSE has the disadvantage of using only one delay, τ , which must be correctly chosen for a good performance of the algorithm. To mitigate such problem, an extension of the method, using several delays, was proposed in [26], leading to the well known SOBI (Second Order Blind Identification) algorithm. Thus, SOBI considers several autocorrelation matrices, obtained with different delays. Such matrices must be jointly diagonalized. An exact joint diagonalization is usually not possible, since eigenvectors of different matrices will not be equal, and also due to estimation errors. The following cost function may be used for optimization JS O B I =



  o f f WRz (τ )WT )

(8)

τ ∈D S

where D S is the set with the delays values being considered and o f f is a function that sums all the off-diagonal elements of a matrix. The cost function given by (8) may be optimized using several algorithms such as least squares, the Temporal Decorrelation Source Separation (TDSEP) [27] or SOBI [26]. The last one is usually preferred in the literature due to its robustness.

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2.2 Wavelet Transform Wavelet Transform (WT) is a data analytic method, as Fourier Transform (FT). However, while the last one approximates a function by decomposing it into sums of sinusoidal functions, the first one makes use of mother wavelets. Both methods are frequently used in signal processing for identifying dominant frequencies in a given analyzed signal. WT is more efficient in dealing with time-frequency analysis, while FT responds only in frequency domain. Therefore, WT provides a more complete analysis, since the time domain information is not lost [28]. Wavelets are functions capable of decomposing and representing another function originally described in the time domain, allowing its analysis in different time and frequency scales. According to [29] a wavelet family can be defined by (9), where a,b represents the mother wavelet, and the variables a and b represent information about scale and time, respectively. 1 a,b (t) = √  a



t −b a

 (9)

To be considered a mother wavelet the function must have the following requirements: ∞ • The area under the curve must be zero, i.e., −∞ (t)dt = 0; ∞ • The energy must be finite, i.e., −∞ |(t) |2 dt < L , L ∈ R The Continuous Wavelet Transform (CWT) of a signal x is defined by (10), showing that the original signal is mapped into a two-dimensional domain (time and scale), which depend on the factors a and b 1 C W T (a, b) = √ a



∞

x(t)( −∞

t −b ). a

(10)

In the same way, the Discrete Wavelet Transform (DWT) is defined by (11), where the parameters a and b change as a function of the parameter m, since a = a0m and b = nb0 a0m ,   1  k − nb0 a0m DW T (m, k) = m , (11) x(n) a0m a0 n where k represents the number of samples. Basically, the filtering process using DWT is based on accepting and rejecting frequency components of a particular signal. When discussing wavelet analysis it is common to use the terms approximation and detail coefficients, where the first one refers to low frequency components, while the last one refers to high frequency components. For three levels of decomposition, the Multi Resolution Analysis (MRA) of the signal x is shown in Fig. 3. Therefore, the MRA aims to divide the frequency spectrum into sub-bands and then treat each one independently [28, 29].

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Fig. 3 MRA—Three level decomposition

Fig. 4 Examples of mother wavelet functions

Wavelet decomposition can be done at several levels and its limit depends on the signal length, the mother wavelet, and the required level of detail. In signal processing the most used mother wavelets are Daubechies (db), Morlets (morl), Coiflets (coif), and Symlets (sym) [28, 29]. Figure 4 presents some examples of mother wavelet functions. The MRA technique can be applied to voltage signals in order to find several levels of approximation and detail, thus making it possible to raise important information about the analyzed signal. Therefore, considering that the analyzed voltage signals are

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taken at the transmission line terminals, this technique can be used for fault location, based on the Traveling Wave principle and the high frequency components extracted by MRA. Currently, the high processing and communication power of intelligent devices and also their extended memory capacity make this kind of implementation possible, resulting in more reliable and accurate fault location schemes [30].

3 Transmission Lines and Traveling Waves 3.1 Transmission Line Faults Modern society is heavily dependent on electricity, independent of the type of consumer, i.e., residential, commercial or industrial. Therefore, the utilities must meet certain performance criteria, defined by regulatory agencies, to avoid penalties. Continuity in electricity supply is an important performance criterion for EES. Nevertheless, faults are frequently occurring due to different factors such as external shortcircuit, lightning, insulation breakdown, among others. Transmission Lines (TL) are the most susceptible elements in EES, due to their extension and exposure to the most varied types of terrain and weather. Thus, efficient fault location schemes play a vital role to restore the EES, improving its reliability and availability. Considering the new IED and their processing and communication power, sophisticated methods for fault location can be implemented, such as the ones based on Traveling Waves (TW) [2]. In general TL faults might happen between phase and ground or between phases, as shown in Fig. 5, where R f is the fault resistance, which can be zero when the fault is solid.

Fig. 5 Fault line types

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3.2 Traveling Waves in Transmission Lines TL faults might result in TW, which move from the fault point to the TL terminals, where they suffer reflections and refractions, that is, one part continues to propagate through the system and the other part propagates back to the fault point. The reflections and refractions occur when the impedance changes (discontinuity points), that is, fault point, TL terminals, open circuits, machines, among others. Bewley’s Lattice diagram represents the TW standard behavior as a time function, showing the direction and distance covered for each TW (reflected and refracted) in different time intervals. Figure 6 illustrates this diagram, assuming a TL with length L and a fault at point d. As can be seen, two waves arise and travel towards the TL terminals where the impedance changes (discontinuity points). Thus, at these moments, part of the waves are refracted to the system and part are reflected back to the fault point. This pattern remains until the TW dissipation due to the TL resistive effect [11]. Assuming that voltage and current signals are measured in both TL terminals and also that the TW propagation speed is known (from the TL physical characteristics), the fault distance can be estimated by using the wavefront arrival times at buses 1 and 2. In general, methods based on TW for fault location use high frequency components, since the useful information to perform this function is found in the high frequency spectrum. With the technology advances IED are more popular and widely used in EES, allowing the development and implementation of new fault location schemes, based on sophisticated signal processing techniques and reliable communication networks. This scenario is typical of future EES, towards the smart grid vision, where faults will be located faster, improving the system availability and resulting in benefits for consumers and the productive sector.

Fig. 6 Bewley’s Lattice diagram of a TL

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4 Fault Detection and Classification Fault location is an important function for TL, however its performance greatly depends on two previous functions, that is, fault detection and fault classification [30]. Thus, it is essential to rely on simple, smart and fast fault detection and classification schemes able to precisely isolate the TL in case of faults. To perform these functions, Euclidean Distance arises as an interesting option to be implemented in IED. The advantages of using this kind of solution are: (a) simplicity, as only basic operations are executed; (b) short response time, when implemented in modern IED with high power processing; (c) reliability and accuracy, by using only voltage signals [12]. Figure 7 shows the flowchart of a possible solution, based on Euclidean Distance, for fault detection and classification in TL. This proposed solution is based on similarity of voltage signals, that is, for EES under normal conditions, the superposition of two consecutive semi-cycles is zero (or it is lower than an acceptable deviation), as presented in Fig. 8a. On the other hand, under fault conditions the superposition of two consecutive semi-cycles is higher than an acceptable deviation, as presented in Fig. 8b. The distance between the first semi-cycle (V1 ) and the inverted second semi-cycle (V2 ) can be measured by Euclidean Distance, thus allowing to differentiate normal (or expected) conditions from faults. For this purpose, let’s consider N samples of one cycle of voltage signal, which can be divided into two sequences of a half cycle each one, as shown by (12) and (13) V1 = [v1 (1) ... v1 (N /2)] V2 = [v2 (1) ... v2 (N /2)]

Fig. 7 Fault location and classification functions based on Euclidean Distance

(12) (13)

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Fig. 8 Superposition of two consecutive semi-cycles: a for non-fault events; b for fault events

The Euclidean Distance between the first and the second half cycle can be calculated by (14) where the operator  ·  corresponds to the Euclidean norm.

 N /2  dist = V1 − (−V2 ) = V1 + V2  = (v1 (k) + v2 (k))2

(14)

k=1

Assuming a three phase system, (14) could be written for each phase, as in (15), where ph should be replaced by a, b or c, depending on the analyzed phase.

dist ph = V ph1 + V ph2 .

(15)

4.1 Fault Detection Function The main goal in determining the dist ph is to measure the EES disturbance level, recalling that for normal conditions (or small transients) this level is lower than a given threshold, while for faults this level is higher than the threshold, generating a trip signal. In a conventional EES, (16) is used for determining the phase with the higher deviation, i.e., the higher Euclidean Distance and so the higher disturbance level. (16) distdet = max(dista , distb , distc ) The threshold value (T V ) depends on the analyzed EES and might be defined by means of simulations and observations. In case of fault, distdet > T V , as depicted in Fig. 9 [12].

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Fig. 9 Flowchart of the developed fault detection method

4.2 Fault Classification Function The Euclidean Distance defined in (15) can also be employed to indicate the faulted phases and then for fault classification in LT. However, for this purpose it is not enough to rely on a single threshold value for dist ph , since the faulted phase might affect the other two phases. Thus, once the fault detection function is performed, (17) is used to verify the ground involvement, by comparing distg with a predefined ground threshold value (T G). distg = (V a1 + V b1 + V c1 ) + (V a2 + V b2 + V c2 )

(17)

Once the ground involvement is confirmed, the fault could be single-phase-ground or double-phase-ground. Otherwise, the fault could be double-phase or three-phase. Assuming that the ground is involved, the phases with the highest value, lowest value and median value of Euclidean Distance must be defined, according to (18). These values will be used to support the fault classification function.

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max dist ph = max(dista , distb , distc ) min dist ph = min(dista , distb , distc )

(18)

med dist ph = med(dista , distb , distc )

Discarding the ground involvement, the fault might only be double-phase (L-L) or three-phase (L-L-L). Therefore, the classification function consists of determining whether the phase with the shortest Euclidean Distance participates in the fault. For this purpose, T V could be used. Nevertheless, when the ground is involved, i.e. faults single-phase-ground (L-G) or double-phase-ground (L-L-G), a more sophisticated method is employed, since the unfaulted phases could also be affected. Initially, the classification distances are normalized, by computing the phases with the shortest and longest Euclidean Distance respectively: dist min = min(dista , distb , distc , distg ), dist max = max(dista , distb , distc , distg ).

(19) (20)

It is important to note that the ground component was also computed. The distance of any normalized phase is determined as min

dist ph(nor m) =

dist ph − dist . dist max − dist min

(21)

Assuming single-phase or double-phase faults, it is concluded that one of the phases is involved and the other one is not, remaining to determine whether the phase with a median distance, computed by (22) is under fault condition or not. med dist ph(nor m) = med(dist a(nor m) , dist b(nor m) , dist c(nor m) )

(22)

In single-phase-faults the median distance tends to be close to zero, while in doublephase faults it tends to be close to one. The flowchart in Fig. 10 summarizes the fault classification method [12].

4.3 Evaluation of the Proposed Fault Detection and Classification Methods To evaluate the proposed methods a TL of 500 kV (at 60 Hz) and 200 km was considered [20]. The adopted TL as well as its main parameters are presented in Fig. 11. This TL was modeled and simulated in PSCAD, thus allowing to generate a large number of different fault and non-fault cases, by changing the parameters presented in Table 1, totalizing 78 fault cases under different operational conditions.

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Fig. 10 Flowchart of the developed fault classification method

Fig. 11 Adopted transmission line

As can be seen in Table 1 the fault cases differ from each other with respect to fault location, fault type, fault resistance, fault inception angle, and bus voltage. By using the software PSCAD, the voltage signals at bus 1 were stored and transferred to Matlab where the proposed scheme was implemented and tested. Still from Table 1, it is possible to verify that solid and non-solid faults were simulated. Figures 12 and 13 show the results obtained by applying the proposed methods fault detection and classification, i.e., the procedures described in Figs. 9 and 10, respectively. It is important to note that the 78 fault cases were correctly detected and classified regardless of fault condition, that is, the resistance, location or type of fault. These figures also show the adopted threshold values (T V and T G), defined by means of simulations and analyses, where the suitable threshold value for discriminating the ground involvement was T G = 0.005 pu.

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Table 1 Adopted power system and faults characteristics Feature Considered values Fault location (distance from bus 1 in km) Fault type Fault resistance () Fault incidence angle (◦ ) Voltage at bus 1 (pu) Voltage at bus 2 (pu) Angle at bus 2 (◦ ) Inductance at buses 1 and 2 (mH)

From 5 to 195 (steps of 5) A; B; C; AB; AC; BC; ABG; ACG; BCG; ABC 0; 50 and 100 −90; −60; −30; 0; 30; 60; 90 1; 1.05 1 −5; −10 5

Fig. 12 Results for fault detection

It is important to mention that the threshold values change depending on the power system characteristics, which means that a new study should be performed when other power systems are considered. When observing Fig. 13, it is clear that some fault cases are close to the threshold values. However, these cases represent limit conditions (worst cases generated by Table 1) and their occurrence is improbable.

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Fig. 13 Results for fault classification

4.4 Considerations on Fault Detection and Classification A method based on the similarity level between two consecutive half cycles, estimated by using Euclidean Distance, was proposed for fault detection and classification purposes. The proposed method is suitable to be implemented in modern IEDs and could be used in practical applications, as it requires low computational effort when compared with most of the current solutions. Moreover, the proposed method does not need communication link or any pre-processing step, correctly responding for fault resistances up to 100 .

5 Fault Location After a fault is detected and classified, locating its position as accurately and fast as possible is the next step, in order to restore the EES as soon as possible. Due to its importance, the development and improvement of TL fault location methods is always an interesting topic of research. Such methods may be divided into two groups: those based on the use of fundamental frequency, and those based on high frequencies. In the first group one may find the most classical methods, those that represent the first solutions proposed to the problem and that have been studied in detail in the literature. In such group we may include methods based on Fourier Transform [31–33], Walsh Transform [34, 35], Kalman Filters [36, 37], Minimum Squares [38], among others.

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In the second group, which includes methods that are based on the use of high frequencies, the Travelling Waves (TW) theory is used [39]. Such methods are more reliable and accurate than the ones based on the use of the fundamental frequency, but they need a high sampling rate which may imply in a higher computational cost and special hardware [40]. In this chapter, we will focus on this group of methods. In recent years, Discrete Wavelet Transform (DWT) has been extensively used for fault location [41, 42], specially with the Multiresolution Analysis (MRA) process, as discussed in Sect. 2.2. Another common technique used exploring high frequencies, and that is also based on the concept of orthogonalization, is the Principal Component Analysis (PCA) [43– 45]. Such technique uses only second-order statistics to analyze the signal. However, in some cases, being able to explore more statistical information of the signals at hand may improve the accuracy of the result. A natural extension of PCA is ICA (discussed in Sect. 2.1). The later exploits higher-order statistics of the signals. A few works including the use of ICA may be found in the literature, but are still very incipient. For example, in [20], the authors use the FastICA algorithm for fault location and Support Vector Machine (SVM) for fault classification. The result obtained was better than using a DWT with an Artificial Neural Network (ANN), but the proposed method has an important drawback since it needs, a priori, a large database to achieve a good performance. In the following, we will discuss how it is possible to use ICA through a simple and robust method for fault location, outperforming DWT-based methods.

5.1 Single Channel ICA Bringing the fault location problem, through the use of TW (Sect. 3), to the BSS (Sect. 2.1) context, the first issue that stands out is the fact that, when dealing with TW, only one or maybe two measured signal(s) (mixtures) are available, depending on whether the measure is obtained on one terminal or both of them. A possible solution to such problem is the use of a variation of ICA, named Single Channel ICA (SCICA), in which each mixture is given by a delayed version of the available measured signal [46]. Thus, instead of using (2) with x(n) defined in (1), the later will be given by: xsc (n) = [x(n) x(n − 1) x(n − 2) . . . x(n − D)]T

(23)

where D is the total number of delays used (equivalent to considering M = D + 1 mixtures). Equation (2) can then be rewritten as: ysc (n) = W xsc (n).

(24)

This approach is useful in the case where the latent components of a single time series must be recovered, thus the name Single Channel ICA. It can be applied when

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Fig. 14 Sliding data window and its updating process

sources are spectrally disjoint from each other [46]. The fault location problem naturally falls within this context, since the measured signal at the terminal of the TL is given by a 60Hz sinusoid. If a fault occurs, the signal will suffer the effect of high frequencies components (in the order of kHz).

5.2 Fault Location with SCICA The first step is the acquisition of a voltage signal, v(n), measured at the TL terminal. The analysis will always consider a data window of one cycle length, updated at each half cycle. Figure 14 shows an example. Next, a modal transformation will be applied to the one cycle length window voltage signal, decomposing it into three single-phase circuits, allowing the analysis of each circuit independently. Applying the modal transformation to the three-phase voltage signals va (n), vb (n) and vc (n) results in the aerial modes vα (n) and vβ (n), and ground mode v0 (n), as shown by ⎡

⎤⎡ ⎤ ⎤ ⎡ 1 1 1 v0 (n) va (n) 1 ⎣vα (n)⎦ = ⎣2 −1 −1 ⎦ ⎣vb (n)⎦ √ √ 3 vβ (n) vc (n) 0 3− 3

(25)

For any fault type, aerial mode α will be used, except in the case of line-to-line faults involving phases B and C (without involving the ground component). Since, in this case, aerial mode α is zero (see (25)), aerial mode β will be used instead. Note that faults in the TL affects all phases, faulty and non-faulty, due to the mutual coupling between them. Figure 15 exemplifies such procedure, showing a faulty three-phase voltage signal and its modal correspondents. Considering vα as being our measured signal, SCICA will be applied with the objective of separating its high frequencies (faulty component). Thus, (23) can be rewritten as xsc (n) = [vα (n) vα (n − 1) vα (n − 2)]T

(26)

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Fig. 15 Voltage signal in case of fault: a Phase voltage; b Modal voltage

Fig. 16 SCICA estimated signals, y1 (n), y2 (n) and y3 (n)

where D has been set to 2 (simulations showed that no improvement is obtained from increasing D). From (24), having three mixture signals, after applying ICA and estimating W, three sources will be estimated ysc (n) = [y1 (n) y2 (n) y3 (n)]T . An example of the recovered sources ysc is shown in Fig. 16. Figure 16a is basically given by the fundamental component. In Fig. 16b, such component is still present. However, it vanishes completely in Fig. 16c, leaving only the high frequency component of the signal. Thus, y3 (n) is exactly what we need to estimate the fault location. Each peak presented in y3 (n) represents the incidence of the TW on the TL terminal, where the signal is being measured. In order to estimate, as precisely as possible, the time instant of the wavefronts, the energy of y3 (n) was obtained, i.e., y32 (n), and the result was normalized in the interval [0, 1] by a min-max normalization. The result signal was named ynor m (n). Now, two

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Fig. 17 Bewley’s Lattice diagram with faults at: a first half of the TL and b second half of the TL

situations are possible and must be analyzed separately: available measurement from only one terminal or synchronized measurements from both terminals are available. 5.2.1

Fault Location Based on Local Measurements

When only the measure of one terminal is available, the time instants of interest are the first and second reflected TW, which corresponds to the first peaks of ynor m (n). However, a particular challenge appears in the definition of the second peak, since it may be due to the remote terminal of the TL instead of the fault point. Figure 17 illustrates such problem. In Fig. 17a, for a fault on the first half of the TL, the first two peaks come from the fault point. However, Fig. 17b shows how the second peak may be due to the remote terminal, when the fault occurs in the second half of the TL, wrongly interfering on the identification of the fault location. Three scenarios are possible: if the fault does not involve the ground component, the fault location may be estimated by d=

v p (tα2 − tα1 ) , 2

(27)

where v p is the positive sequence propagation speed and tα1 and tα2 are the time instants of the first and second peaks, respectively, of ynor m (n) obtained from the aerial mode α (or β, depending on the fault) measured at bus 1. If the fault involves the ground component, the first step is to determine in which half of the TL it occurred [47]. This procedure can be done by comparing the incidence times of signals from aerial and ground modes, from which it is possible to conclude if the fault occurred in the first or second half of the TL. If the fault occurred in the first half, (27) can be used. However, if it was on the second half, d must be estimated as v p (tα2 − tα1 ) , (28) d=L− 2 where L is the TL length.

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5.2.2

319

Fault Location Based on Synchronized Measurements

When measurements from both terminals are available, using the information of the first wavefront at each terminal is sufficient to estimate the fault location. Estimation in this case is more precise than the one obtained with the local measurement, and faults in the second half of the TL do not pose a problem. On the other hand, this scheme is more costly and complex than the preceding one, since measurements must be synchronized. In this case, SCICA is applied to the aerial mode α (β if needed) of the signals measured in both terminals, independently. Analyzing the time instant of the first  . Thus, the fault location may be estimated peak in each case will result in tα1 and tα1 as  ) v p (tα1 − tα1 . (29) d=L+ 2

5.3 Performance Analysis of the SCICA method In this section we will only analyze fault location, considering that the fault has already been detected and correctly classified. The SCICA source separation was implemented using the SOBI algorithm with D = 2 (Eq. 26) mixtures and a set of five time delays in (8), i.e., D S = 5. For comparison purposes, DWT was also applied to the same scenario, since it is a classical method for fault location. The characteristics of the simulated TL is given in Fig. 11. Several fault cases were generated varying fault location, type, resistance, among others, as shown in Table 1. The voltage signals at the TL terminals were sampled at 100kHz and stored. PSCAD was used for TL modeling and simulation, while the voltage signals were stored for post-processing in Matlab. Performance was measured by the error between the estimated distance d and the true value d0 Err or (%) = 100

|d0 − d| L

(30)

Figures 18 and 19 show the results obtained for the case of one local measurement (bus 1) and two synchronized measurements, respectively. Figure 18 shows seven outliers cases in which the error was higher than 3%, five for DWT and two for SCICA. These are cases where the pre-step of determining if the fault occurred on the first or second half of the TL failed. Table 2 summarizes the results, showing the average error (obtained excluding the outliers). We can see that SCICA outperforms DWT by over 17%, also presenting fewer outliers. In the synchronized measurements case, given by Fig. 19, the difference in performance between DWT and SCICA is not so important, but SCICA still outperforms DWT in 3%. Figure 19 shows how the greater differences in performance occurs specially for faults near the middle of the TL.

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Fig. 18 Performance of DWT and SCICA with measurements in one terminal: a No fault resistance, b Fault resistances of 30, 50 or 100 

Fig. 19 Performance of DWT and SCICA with measurements in both terminals: a No fault resistance, b Fault resistances of 30, 50 or 100  Table 2 Average errors obtained for local and synchronized measurements, excluding outliers Algorithms One terminal Two terminals Errors (%) Outliers Errors (%) Outliers DWT SCICA

0.98 0.81

5 2

0.73 0.70

0 0

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5.4 Considerations on Fault Location In this section we have discussed the fault location problem. We have focused on methods that explore high frequencies generated on the signal by the fault. In this context, DWT and PCA are the most used techniques in the literature. This section shows how ICA may also be very useful in solving such problem. A simple and robust method, based on Single Channel ICA, was presented. The method has shown a better performance than DWT in a simulated TL context. In addition, when compared to other literature methods, it has the advantage of using only voltage signals, it does not need a previous database for presenting a good performance and it presents a low computational burden.

6 Conclusion This chapter addressed the three important stages that must be considered when dealing with a fault in an EES: detection, classification and location. The reliability and robustness of an EES depends on the efficacy of these three stages, detecting, classifying and locating the fault as fast and precisely as possible. Here we have briefly discussed literature results on treating these problems and have presented and discussed in detail recently proposed methods, approaching the three problems through simple solutions. For fault detection and classification, it is shown how the use of Euclidean Distances on measured voltage signals are able to provide fast and reliable results. On the other hand, for fault location, a simple method based on SCICA is presented, showing how it outperforms the classical DWT method.

References 1. Glover JD et al (2022) Power system analysis and design. Cengage learning 2. Horowitz SH, Phadke AGW (2014) Power system relaying 3. Thomas MS, McDonald JD (2015) Power system SCADA and smart grids. Taylor Francis Group 4. Petite FSV et al (2021) A comprehensive backup protection for transmission lines based on an intelligent wide-area monitoring system. Int Trans Electr Energy Syst 1:1–21. https://doi.org/ 10.1002/2050-7038.12870 5. Gupta OH, Tripathy M (2015) An innovative pilot relaying scheme for shunt-compensated line. In: IEEE transactions on power delivery, vol 30.3, pp 1439–1448. https://doi.org/10. 1109/TPWRD.2015.2394353 6. Pérez FE, Orduña E, Guidi G (2011) Adaptive wavelets applied to fault classification on transmission lines. In: IET generation, transmission & distribution, vol 5.7, pp 694–702. https:// doi.org/10.1049/iet-gtd.2010.0615

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7. Prasad CD, Nayak PK (2019) A DFT-ED based approach for detection and classification of faults in electric power transmission networks. Ain Shams Eng J 10(1):171–178. https://doi. org/10.1016/j.asej.2018.02.004 8. Kintali M et al (2019) Euclidean metric based fault diagnosis in power transmission line. Int J Innov Technol Explor Eng 9.2S3:226–231. https://doi.org/10.35940/ijitee.B1057.1292S319 9. Akmaz D et al (2018) Transmission line fault location using traveling wave frequencies and extreme learning machine. In: Electric power systems research, vol 155, pp 1–7. ISSN: 03787796. https://doi.org/10.1016/j.epsr.2017.09.019 10. Jamil M et al (2014) Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line. In: Applied soft computing, vol 19, pp 322–332. ISSN: 1568-4946. https://doi.org/10.1016/j.asoc.2014.02.020 11. Naidu O, Pradhan AK (2019) A traveling wave-based fault location method using unsynchronized current measurements. In: IEEE transactions on power delivery, vol 34.2, pp 505–513. https://doi.org/10.1109/TPWRD.2018.2875598 12. Alencar GT, Santos RC, Neves A (2022) Euclidean distance-based method for fault detection and classification in transmission lines. J Control Autom Electr Syst 33:1466–1476. https:// doi.org/10.1007/s40313-022-00918-x 13. Cherry EC (1953) Some experiments on the recognition of speech, with one and with two ears. J Acoust Soc Am 25(5):975–979 14. Héault J, Jutten C, Ans B (1985) Détection de grandeurs primitives dans un message composite par une architecture de calcul neuromimétique en apprentissage non supervisé. In: 10 Colloque sur le traitement du signal et des images, FRA, 1985. GRETSI, Groupe d’Etudes du Traitement du Signal et des Images 15. Ferréol A, Chevalier P, Albera L (2004) Second-order blind separation of first-and second-order cyclostationary sources-application to AM, FSK, CPFSK, and deterministic sources. In: IEEE transactions on signal processing, vol 52.4, pp 845–861 16. Virtanen T (2007) Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. In: IEEE transactions on audio, speech, and language processing, 15.3, pp 1066–1074 17. Poh M-Z, McDuff DJ, Picard RW (2010) Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. In: Optics express, vol 18.10, pp 10762–10774 18. Oosugi N et al (2017) A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal. Neural Netw 93:1–6 19. Chen C, Wang Z (2006) ICA and factor analysis application in seismic profiling. In: 2006 IEEE international symposium on geoscience and remote sensing, pp 1560–1563. https://doi.org/10. 1109/IGARSS.2006.402 20. Almeida AR et al (2017) ICA feature extraction for the location an classification of faults in high-voltage transmission lines. Electr Power Syst Res 148:254–263 21. Comon P, Jutten C (2010) Handbook of blind source separation: independent component analysis and applications. Academic Press 22. Romano JMT et al (2010) Unsupervised signal processing: channel equalization and source separation. CRC Press 23. Hyvärinen A, Karhunen J, Oja E (2004) Independent component analysis, vol 46. Wiley 24. Comon P (1994) Independent component analysis, a newconcept? In: Signal processing, vol 36.3, pp 287–314 25. Tong L et al (1990) AMUSE: a new blind identification algorithm. In: IEEE international symposium on circuits and systems. IEEE, pp 1784–1787 26. Belouchrani A et al (1997) A blind source separation technique using secondorder statistics. In: IEEE transactions on signal processing, vol 45.2, pp 434–444 27. Ziehe A, Müller K-R (1998) TDSEP-an efficient algorithm for blind separation using time structure. In: Niklasson L, Bodén M, Ziemke T (eds) ICANN 98. Springer, London, pp 675– 680 28. Debnath L, Shah FA (2015) Wavelet transforms and their applications. Birkhauser

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29. Daubechies I (1992) Ten lectures on wavelets. Society for industrial and applied mathematic 30. Rebizant W, Szafran J, Wiszniewski A (2011) Digital signal processing in power system protection and control. Springer 31. Li M et al (2017) Sixth harmonic-based fault location for VSC-DC distribution systems. In: IET generation, transmission and distribution, vol 11.14, pp 3485–3490 32. Phade AG, Hlibka T, Ibrahim M (1977) Fundamental basis for distance relaying with symmetrical components. In: IEEE transactions on power apparatus and systems, vol 96.2, pp 635–646 33. Saha MM, Izykowski J, Rosolowski E (2010) Fault location on power networks. Springer, London 34. Ferrer HJA, Verduzco ID, Martinez EV (1996) Fourier and Walsh digital filtering algorithms for distance protection. In: IEEE transactions on power systems, vol 11.1, pp 457–462 35. Sharma P, Ali S, Kapoor G (2018) Fault detection on series capacitor compensated transmission line using Walsh Hadamard transform. In: IEEE international conferences on computing power and communication technologies, pp 763–768 36. Girgis AA (1982) A new kalman filtering based digital distance relay. In: IEEE transactions on power apparatus and systems PAS-101.9, pp 3471–3480 37. Fan R et al (2018) Precise fault location on transmission lines using ensemble kalman filter. In: IEEE transactions on power delivery, vol 33.6, pp 3252–3255 38. Sachdev MS, Baribeau MA (1979) A new algorithm for digital impedance relays. In: IEEE transactions on power apparatus and systems PAS-98.6, pp 2232–2240 39. Bewley LV (1963) Traveling waves on transmission systems. Wiley 40. Zimmerman RD et al (2011) Steady-state operations, planning, and analysis tools for power systems research and education. In: IEEE Transactions on power systems, vol 26.1, pp 12–19 41. Saber A, Emam A, Amer R (2016) Discrete wavelet transform and support vector machinebased parallel transmission line faults classification. In: IEEJ transactions on electrical and electronic engineering, vol 11.1, pp 43–48 42. Arroyo SM, García-Gracia M, Montañés A (2019) The half-sine method: a new accurate location method based on wavelet transform for transmission- line protection from singleended measurements. In: Energies, vol 12.17 43. Jafarian P, Sanaye-Pasand MA (2010) Traveling-Wave-Based protection technique using wavelet/PCA analysis. IEEE Trans Power Deliv 25:588–599. https://doi.org/10.1109/TPWRD. 2009.2037819 44. Aguilar R, Perez F, Orduña E (2011) High-speed transmission line protection using principal component analysis, a deterministic algorithm. In: IET generation, transmission and distribution, vol 5.7, pp 712–719. https://doi.org/10.1049/iet-gtd.2010.0771 45. Alsafasfeh Q, Abdel-Qader I, Harb A (2012) Fault classification and localization in power systems using fault signatures and principal components analysis. Energy Power Eng 04:506– 522. https://doi.org/10.4236/epe.2012.46064 46. Davies ME, James C (2007) Source separation using single channel ICA. Signal Process 87:1819–1832. https://doi.org/10.1016/j.sigpro.2007.01.011 47. Abur A, Magnago FH (2000) Use of time delays between modal components inwavelet based fault location. Int J Electr Power Energy Syst 22:397–403. https://doi.org/10.1016/S01420615(00)00010-7

Active Power Filters Applied to Smart Grids: Harmonic Content Estimation Based on Deep Neural Network Claudionor Francisco do Nascimento, Alfeu Joãozinho Sguarezi Filho, Amilcar Flamarion Querubini Gonçalves, Augusto Matheus dos Santos Alonso, Luiz Gustavo Reis Bernardino, Paulo Fernando Silva, and Wesley Angelino de Souza Abstract The increasing penetration of power electronics loads in smart grids inherently leads to severe concerns about power quality (PQ) disturbances. This chapter presents the principles of synthesizing control references for an active power filter (APF), which is placed in a smart grid comprising distortion loads, aiming at achieving PQ enhancement and compliance with standardized indexes. In addition, it is argued that the APF control system requires harmonic content identification to generate the targeted compensating currents. Thus, to achieve the disturbance recognition expected for synthesizing control references, harmonic analysis methods can be devised by automation tools and artificial intelligence (AI). For instance, it is demonstrated here that deep neural networks (DNN) can be used in the main pattern recognition stage, simplifying the harmonic content estimation process. The DNN-estimated harmonics are then directly used within the APF control system to C. Francisco do Nascimento (B) · A. Flamarion Querubini Gonçalves · P. Fernando Silva Federal University of São Carlos (UFSCar), São Carlos-SP, Brazil e-mail: [email protected] A. Flamarion Querubini Gonçalves e-mail: [email protected] P. Fernando Silva e-mail: [email protected] A. J. Sguarezi Filho Federal University of ABC (UFABC), Santo André-SP, Brazil e-mail: [email protected] A. Matheus dos Santos Alonso University of São Paulo (USP), São Carlos-SP, Brazil e-mail: [email protected] L. Gustavo Reis Bernardino Federal University of São Carlos (UFSCar), Sorocaba-SP, Brazil e-mail: [email protected] W. Angelino de Souza Federal University of Technology – Paraná (UTFPR), Cornélio Procópio-PR, Brazil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_12

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compensate disturbances aiming at reducing the total harmonic distortion in a smart grid. Finally, this chapter also presents case studies using experimental load currents to depict the feasibility of the DNN-based method.

1 Introduction Smart grids represent the next technological leap from the conventional grid [1, 2]. These networks are considered a promising solution to modernize electricity distribution since they take advantage of information and communication technologies, which can improve the efficiency, security, and stability of the conventional power grid [3]. Figure 1 presents, for instance, an example of a microgrid inserted within the smart grid paradigm, comprising multiple loads and converter interfaced distributed energy sources, as well as an active filter. With regards to power quality (PQ), a smart grid must also follow standards and grid codes to respect compliance requirements in order to achieve the aforementioned operational benefits. Otherwise, the network will be inherently prone to instability. Harmonic distortion is, for instance, one of the most common and yet challenging PQ problems, which must be tackled by the technical and scientific community. In general, the harmonic pollution generated by single-phase and three-phase non-linear (i.e., power electronics-based) loads is individually small in power [4]. Nevertheless, the dense penetration of such loads in electrical systems can cause significant harmonic distortion problems. This harmonic pollution is not only restricted to industrial environments but is also present within the residential and commercial grid perspectives. Furthermore, the flow of harmonic current through the system results in a number of undesirable effects [5, 6].

Fig. 1 AC low voltage smart microgrid with non-linear local loads

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Harmonics can be produced by static converters or saturated reactors, resulting from the switching process of power electronics-based equipment or being caused due to the magnetizing current of transformers [7, 8]. In this work, only non-linear loads based on static converters are taken into consideration. The unwanted effects of harmonics in electrical systems are straightforwardly evidenced by additional heating losses (e.g., in rotating machines), inappropriate interference with the operating mechanisms of protective equipment, resonance problems, and increased additional heating losses in parallel capacitor banks. Other issues arising from harmonic distortions are also related to overvoltages in lighting systems, degradation of the accuracy of induction-type active energy meters, undesired effects on the operational characteristics of protection relays, noise excitation in communication systems, so forth [9, 10]. Moreover, although the perspective of residential systems indicates that consumers’ perception is mostly concerned about either failure or interruption in the operation of household equipment, power utilities take the cost of degradation of the electrical grid as the major motivation to mitigate harmonics [11]. Despite the fact that harmonic pollution is a present and unquestionable challenge that affects modern electric power grids, the desire to prevent non-linear behaviors in voltages and currents has been studied since many decades ago [12]. Thus, the motivation for the development of auxiliary circuits (i.e., embedded in a pre-existing operating load) and independent systems acting as power conditioners (e.g., filters) have been boosted over the years. Nonetheless, avoiding harmonic distortion under the presence of non-linear loads is a difficult task, especially in low-power grids, due to multiple electrical interactions occurring among harmonic sources, attenuators and grid elements (e.g., line impedances). So far, several research efforts have been carried out in the power electronics field to mitigate the issues related to harmonic distortion in electrical systems [4, 13–15]. There are practically two methods for determining the solution to the problem. The first one is based on the preventive solution, which aims at improving the performance of the equipment acting as a harmonic source. In this case, design modifications are performed to reduce the harmonic content generated by the non-linear load, which is usually devised by adjustments in the equipment’s circuit (e.g., the voltage rectifier pre-regulators) [14, 16]. The second one comprises the corrective techniques that use filters for harmonic compensation (e.g., RLC filters tuned at the 3rd harmonic order, being allocated in parallel with the load) [17]. As an example, the adoption of filters to attain harmonic compensation stands on the corrective perspective, i.e., they are not part of the non-linear load circuit existing in the system. Hence, such filters act directly on the electrical grid in which this load is inserted. The problems related to harmonic distortion are traditionally solved in a power grid by deploying power conditioners (i.e., filters), aiming at canceling the circulation of harmonic components in the circuit. There are two general classes of filters for the mitigation of harmonic distortions. The first class is based on conventional passive filters, which is a solution susceptible to trigger resonances due to interactions with grid impedances and other system loads [18]. Such filters are unsuitable for systems comprising loads that are sensitive to variable harmonic content [19, 20]. On the

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other hand, the second class comprises active or hybrid (i.e., a combination of active and passive topologies) power filters [21], which rely on the control of switching power interfaces to act on PQ improvement purposes [22]. Thus, due to their flexible control operationalities, the APF has been consolidated as an effective solution in correcting harmonic distortion [9, 23].

2 Fundamentals of Shunt Active Power Filters (SAPF) The APF is usually constituted of a voltage source inverter connected to the electrical grid [24], having the main function of mitigating either voltage or current harmonic distortions depending on its topology [24, 25]. In addition, APF can be classified as series or parallel (i.e., shunt) [26], which are generally employed to tackle voltage or current disturbances, respectively. However, a SAPF is the topology that has been most widely used. APF can also be classified according to the structure of the electrical grid, meaning that they can be designed for single-phase or three-phase applications. For instance, in three-phase circuits, APF can be classified as threephase three-wire (delta) and three-phase four-wire (star) [27]. Note that the topology of SAPF, which is presented in Fig. 2, comprises a voltage source inverter (VSI) connected in parallel with the load [28]. Hence, the SAPF’s goal is to inject specific currents into the PCC, allowing to cancel the harmonic current components drawn from the grid [29–31].

Fig. 2 Shunt active power filter application on a smart grid with non-linear load

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The compensation characteristics of a SAPF are primarily defined by the strategy adopted for estimating the harmonic content of the load currents, as well as by the approach responsible for the synthesis of the reference current used to control the device. The determination of such reference currents has been done in the literature through two main approaches: (i) the so-called time domain approach e.g., using the p-q theory [32] or other methods such as the conservative power theory (CPT) [33]; and (ii) the frequency domain approach (e.g., by using the Fourier analysis [34]). It is important to mention that the frequency domain strategy makes it possible to determine the harmonic components selectively [35]. Consequently, the compensation can be either limited to a certain harmonic order (e.g., the 20th component) or it can consider a group of components. The greater the number of harmonic components, the greater the number of calculations required to synthesize the reference currents, leading to a more complex computational implementation of the digital control of the APF.

2.1 Reference Current of the SAPF The term “active filter” is generic and can be applied to a group of power electronics circuits formed by semiconductor devices devising power switching. These types of equipment work together with energy storage circuits characterized by the presence of capacitors and inductors. The APF topology typically depends on the intended application, requiring an exact design to the targeted power circuit [25]. Basically, an APF detects the harmonic current of the line waveform or the nonlinear load (or set of loads), then generates an adequate reference current. This current is used by the APF control system to cancel these harmonics. Consequently, such intervention either eliminates completely or partially attenuates harmonic distortions in the grid [5]. There are two approaches to generate the reference current of an SAPF, which are the following: time domain and frequency domain. The SAPF reference current generation strategy based on the p-q theory is considered in the time domain because it uses instantaneous values [9]. On the other hand, the frequency domain strategy uses amplitudes and phase angles generally defined by the DFT or FFT [36]. DFT is a traditional technique widely used in the spectral analysis of the load current [36]. The FFT is used to quickly calculate DFT in order to determine the harmonic components in real time and with less computational effort. In this chapter, the efficiency of DFT and FFT is not addressed. Truncated FFT has also been used in order to reduce the computational time response intrinsic to this technique. This is because this response becomes smaller as the number of harmonics involved decreases. An alternative tool to the FFT is the implementation of AI algorithms applied to the process of estimating the harmonic content of currents. Among the most used AI methods lies the ANN. Such an approach implements an intelligent system that is trained offline using the data collected from the Fourier analysis of the load currents (i.e., the ANN design is decoupled from the APF operation process). Thus, based on

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the Fourier analysis, the current signals can be reconstructed considering up to the twentieth harmonic component. Soon after, the reconstructed signal is presented at the ANN input to carry out the training process. After training, the ANN identifies each harmonic component online (coupled to the process) based on the load current amplitudes sampled and presented at the ANN input. The implementation of AI algorithms presents some advantages over traditional harmonic estimation methodologies, such as being able to separate harmonic components in the time of half a cycle of the line voltage, in addition to continuously following the harmonics of the load current and transferring this data to the system responsible for acquisition and control. In some cases, ANN can identify the main Fourier series coefficients in just one cycle of the grid voltage waveform. There is also the possibility of using one or more architectures of ANN together in the harmonic compensation system: for instance, an adaptive linear element (ADALINE) for the determination of harmonics and a multilayer perceptron for the control of the active filter. This chapter has the main goal of presenting a method based on ANN to determine the low-frequency (i.e., up to the 20th order) harmonic components of non-linear load currents, which serve to determine the reference currents of a SAPF, aiming at achieving selective compensation. Therefore, in this chapter, SAPF operation is used as an application to evaluate the method for determining harmonic components.

3 Primary Control Structure—Voltage Source Inverter Control The control strategies employed on SAPFs depend directly on the circuit topology, as well as on the control objectives. The operational targets are classified by the current components to be corrected and by the required response to the grid’s voltage distortion [26, 37]. For instance, there are several current control techniques for a SAPF: the hysteresis controller, the deadbeat digital controller, and the linear controller in rotating frames. All these mentioned techniques present acceptable performance; however, they differ mainly in the delay caused by the control processing [38]. This section explores the operating principle and linear control structure of the three-phase SAPF, considering its interface based on a VSI. The parts that compose the primary control of the SAPF are the PWM, the PLL, the current controller, and the DC voltage controller. As observed in Fig. 3, such a structure adjusts the inverter gains so that the VSI can synthesize the harmonic reference current generated by the intelligent algorithm (i.e., the one responsible for the extraction of harmonic components). Each of these parts are explained as follows.

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Fig. 3 Structure of the SAPF control

3.1 PWM Application in SAPF The modulator generates the PWM signal by comparing the control (i.e., modulating) signal with the carrier signal. The modulation technique depends on the structure of the static converter electrical circuit, knowing that for three-phase SAPF, the main techniques are the SPWM and the SVPWM. Implementing SPWM is simpler than SVPWM because it only requires comparing the control signal with a triangular signal. On the other hand, for the SVPWM, it is necessary to verify the position of spatial vectors to determine the switching states of the VSI. Nonetheless, the advantage of the SVPWM approach is the lower DC bus voltage level required to modulate the control signal, resulting in a voltage gain of approximately 15% higher when compared to SPWM. SPWM and SVPWM techniques have already been widely explored in the literature in the past and will not be discussed here. The reader will find extensive discussion in [39, 40], as well as discussions on the digital implementation of PWM.

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3.2 SAPF Synchronization Using PLL The PLL algorithm is required to certify that the SAPF operates synchronized with the grid voltage, providing adequate compliance with grid codes and proper synthesis of the current compensation components. Hence, the PLL generates a signal synchronized with the grid and provides the phase angle θ or the grid’s fundamental frequency ω. Consequently, it is possible to generate a current reference (i.e., internally to an embedded system, such a DSP), which is in phase with the grid voltage. The desired characteristic of the PLL is that its dynamics should be fast enough to not compromise the compensation of harmonic distortions. Moreover, it should provide the precise synchronization angle when the grid voltage is distorted and/or unbalanced [41]. Herein, it is considered that the adopted PLL algorithm implemented for the SAPF meets the requirements to generate the synchronization reference with the grid.

3.3 Current Control Loop The current control loop is the innermost and the fastest loop in the control system designed for the SAPF, being responsible to adjust the output current according to the desired compensation reference. The reference current (i ∗ ) is collected from the harmonic components (i h ) provided by the AI algorithm, and it is summed with the reference fundamental current (i 1∗ ). The latter must be calculated to maintain the VSI’s DC link voltage (VDC ) stable, being obtained through the implementation of a PI controller and a trigonometric function that uses as input the signal (θ ; i.e., given by the PLL) in phase with the grid voltage. In this chapter, the current controller is based on the static referential control structure using PR regulators. The PR controller is implemented in the stationary referential frame (i.e., the αβ frame), being an alternative to the conventional PI controller, allowing to achieve zero steady-state error and providing infinite gain at the interest harmonic frequencies [42, 43]. In Yuan et al. [42], the authors use a PR controller (named stationary-frame generalized integrators), showing that static reference frame control has the advantage of working under balanced or unbalanced voltage conditions at the PCC. Moreover, to achieve a better performance of the PR controller, the resonance frequency controller (ωi ) must be calculated online from the synchronized frequency provided by the PLL. As a result, they guarantee that the resonant term always acts in the ωi frequency even when there are deviations in the grid frequency [44].

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The open loop transfer function of the current (Fi (s)) is given by Eq. (1). ⎛

⎞ K s i1 ⎠ G(s) 1 Fi (s) = ⎝ K p1 + 2 2 R + sL s + ωi i=3,5,7,9,11 

(1)

In this equation, K p1 and K i1 represent the proportional and integral gain, respectively, of the PR controller. The VSI output impedance transfer function is simplified by the term 1/(R + s L) and G(s) is the VSI voltage transfer function. The value of G(s) will depend on the topology and the PWM used of the converter. In parentheses is the PR transfer function adapted for i odd harmonics. The PR controller allows, in theory, multiple resonant terms to be added at the frequencies of interest (ωi ), but terms are generally used at the odd-order harmonics up to the 15th order. The maximum frequency to be compensated must respect the maximum sampling rate and the cutoff frequency of the VSI output filter [45]. The reader can find in [40, 44, 46] more information about this controller and how to tune the parameters from (1).

3.4 DC Voltage Control Loop The DC voltage control loop regulates the bus voltage during the stage where the VSI acts as a rectifier. The regulation of the DC voltage level is essential to ensure that the VSI can synthesize the compensation current of the SAPF. The transfer function of the control plant is obtained from the power balance between the DC and AC sides, disregarding losses. It can be assumed that the active power flow from the grid to the filter is equal to the active power on the DC side [47]. Equation (2) represents the open loop transfer function of the DC voltage (VDC ) loop (Fv (s)) using PI controller. In this equation, K p represents the proportional gain, K i represents the integral gain of the PI controller, G i (s) represents the closed loop transfer function of the current and in parentheses is the plant transfer function, where Vg− pk is the maximum value of the voltage at the PCC, C is the capacitance of the DC bus and 3/2 is a proportionality constant that depends on the topology of the power converter (in this case it is represented for a three-phase power converter).   K p s + Ki 3 Vg− pk (2) G i (s) Fv (s) = s 2 sC VDC Regarding the dynamics of this loop, the choice of the closed loop cut-off frequency for the design of the DC controller gain must guarantee the decoupling between the voltage control loop and the current control loop. Thus, it is essential to design the controllers so that the DC voltage control loop is slower than the current control loop [40].

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4 Harmonic Content Detection Harmonics are sinusoidal voltages or currents in which the frequencies are integer multiples of the fundamental frequency [48]. The main effects caused by harmonics are: malfunction of control devices, telephone interference, additional losses in power lines, and the reduction of equipment lifetime together with increased losses in power systems [48]. The harmonics can be originated from the non-linearity of the magnetization of the transformer, rotating electrical machines, distortion caused by electric arc devices, single-phase rectifiers, three-phase current source converters, three-phase voltage source converters, AC drives fed by frequency inverters, thyristor controlled reactors, phase modulated controls, AC regulators, among others [10]. The non-sinusoidal signal f (t) is depicted in Eq. (3), where Fcc is the continuous component, Fn is the amplitude value of the n order component, φn is the phase angle of the n order component, ω is the angular velocity and t is time. f (t) = Fcc +

n max 

Fn cos(nωt + φn )

(3)

n=1

According to Eq. (4), the value of Fcc is equal to the mean value of the nonsinusoidal function at period T provided all harmonics are sinusoidal [10, 49]. 1 T f (t)dt (4) Fcc = T 0 It is usual to use symmetrical components to describe the behavior of a threephase system [49]. Assuming a balanced system of positive phase sequence (ABC), Eq. (5) depicts the expressions of the fundamental currents (i a(1) , i b(1) e i c(1) ), where Ia(1) , Ib(1) e Ic(1) are the maximum values of the fundamental currents. The negative phase angles show that the phasors rotate clockwise in the space-time plane. ⎧ (1) i (t) = Ia(1) cos(ωt) ⎪ ⎨ a i b(1) (t) = Ib(1) cos(ωt − 120◦ ) ⎪ ⎩ (1) i c (t) = Ic(1) cos(ωt − 240◦ )

(5)

For example, the third harmonic has no angular phase shift between the three phases, according to Eq. (6). This case is called zero-sequence. Zero sequence components are essential in four-wire systems with the star connection side of the transformer grounded. Here, there is a circulation of zero sequence currents in the neutral conductor [49].

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⎧ (3) i (t) = Ia(3) cos(3ωt) ⎪ ⎨ a i b(3) (t) = Ib(3) cos 3(ωt − 120◦ ) = Ib(3) cos(3ωt − 360◦ ) = Ib(3) cos(3ωt) ⎪ ⎩ (3) i c (t) = Ic(3) cos 3(ωt − 240◦ ) = Ic(3) cos(3ωt − 720◦ ) = Ic(3) cos(3ωt)

(6)

When the lag between the angles is positive, as with the fifth harmonic in Eq. (7), the phasors rotate counterclockwise, i.e. opposite to the direction of the fundamental component. This situation is called negative phase sequence [48, 49]. ⎧ (5) i (t) = Ia(5) cos(5ωt) ⎪ ⎨ a i b(5) (t) = Ib(5) cos 5(ωt − 120◦ ) = Ib(5) cos(5ωt − 600◦ ) = Ib(5) cos(5ωt + 120◦ ) ⎪ ⎩ (5) i c (t) = Ic(5) cos 5(ωt − 240◦ ) = Ic(5) cos(5ωt − 1200◦ ) = Ic(5) cos(5ωt + 240◦ ) (7) Regarding quantifying the harmonic content through a single value, the most widely used index is the THD [10]. Such a measure is the harmonic components’ effective value of a distorted waveform, e.g. the voltage signal (T H DV ), which is defined according to (8). In (8), V (h) is the effective value of the h harmonic voltage, and V (1) is the effective value of the fundamental voltage component.    ∞  (h) 2  V T H DV =

h=2

V (1)

(8)

Harmonic voltages and currents circulating in the electrical system can cause resonances, effects on rotating machines, effects on power plants, interference in control systems, interference in protecting electrical systems, effects on consumer equipment, interference with communication networks and noise in electric motors [10]. Harmonic monitoring [10] is used to quantify this phenomenon and involves capturing and processing voltage and current signals at various points in the electrical system. Once gathered, this information can be sent to a monitoring system, and a decision-making process [10]. Once the information related to harmonics is available, the next stage is to mitigate them if there is interest. There are ways to perform this task. Passive, tuned, damped, DC, and active filters can be used. The active filters are designed to eliminate specific harmonics and usually need an instantaneous identification of these components at the PCC. Some standards, such as IEEE 1547 [50], IEC 61727 [51], and IEC 610003-2 [52] establish the limitation of harmonic circulation in electrical and microgrids in order to guarantee an adequate minimum PQ level. The harmonic analysis estimates the magnitudes (amplitudes) and phase angles of the fundamental and the harmonic components of a periodic waveform [10]. The Fourier series can perform the harmonic analysis and establishes the relationship between a function in the time domain and the frequency domain [48]. The Fourier

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series is a special case of the Fourier transform applied to a periodic signal, such as the voltage and current waveforms. Waveform information is often obtained as a function of sample time and is represented by a time series of amplitudes, separated by fixed time intervals of limited duration [10, 48]. The DFT is used when working with such data discretized [10]. Implementing the DFT through the so-called FFT [10] comprises the basis of most modern spectral and harmonic analysis systems. FFT has become popular because of its simplicity and computational agility. However, in many practical cases, as there is the presence of interharmonics, deviation from the fundamental frequency, presence of noise not multiples of the fundamental frequency, and time-varying signals, among others, its performance does not reach the desired level of accuracy in some applications, mainly related to power electronics [53–56]. FFT needs at least one full cycle of the voltage or current, depending on the desired resolution. For example, 10 to 12 cycles are needed for a resolution of 5 H z [57–59]. Transient and stationary voltage and current signals captured from electrical systems, when viewed using limited data (because of finite sampling capacity), introduce errors in the frequency spectrum of that signal [10, 48]. Some more recent methods using automation and the AI can deal satisfactorily with this problem. ANN are used to automate disturbance and recognition, and they can be used in the processing stage to recognize patterns [10, 48, 60].

5 Harmonic Content Determination A variety of techniques have been applied to extract the frequency spectrum of voltage or current waveforms, such as: • • • • • • • • • • • •

Fourier series and coefficients; Simplifications resulting from waveform symmetry; The complex form of the Fourier series; Convolution of harmonic phasors; Fourier transform; Sampled time functions; Discrete Fourier Transform; Nyquist frequency and Aliasing; Fast Fourier Transform; Slicing functions; Wavelet transform; Automation of disturbance recognition (e.g. ANN).

For digital applications, in which the signals are discretized, the DFT/FFT method is extensively used [61–64]. In such cases, the frequency domain spectrum is a function that uses the signal acquisition in the time domain, and a pair of Fourier transforms is obtained according to (9).

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⎧ N −1 ⎪ 1  ⎪ ⎪ X ( f ) = x(tn )e− j2πkn/N ⎪ k ⎪ ⎨ N n=0 N −1 ⎪  ⎪ ⎪ ⎪ x(t ) = X ( f k )e j2πkn/N ⎪ n ⎩

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(9)

k=0

in which N is the number of samples per period, X ( f k ) represents the function of the component in the frequency domain, and x(tn ) represents a sample of the function in the time domain. The element 2nπ/N represents a clockwise rotation, such that n = 0, 1, 2, ..., (N − 1) [10]. For a high N , the multiplications involved in DFT can take a high computational cost and consume a lot of computational effort. The FFT arises as an alternative since it considerably reduces the number of multiplications to fetch the solution [10]. In practice, an algorithm with a mathematical background has been used to resolve FFT. The use of ML was restricted years ago due to the unavailability of computational resources capable of processing the amount of information required. But, with the greater availability of resources, it is possible to extend the use of heuristic techniques that require significant computational effort, such as ANN, in the signal processing of power systems [65]. Some proposals for automating the disturbance recognition and identification process have been developed to improve the speed, reliability and ease of obtaining and storing information [10], as is exposed in the following section.

5.1 ANN Application Several recent research efforts have been published applying ML techniques, ANN and DL to identify PQ related disturbances. In [13, 66–69], the authors have exposed approaches for applying DNN to the harmonic detection process with the generation of the reference current for an APF. Test results reinforce that the methods can estimate low-frequency harmonics accurately. In [70, 71], ML-based approaches are applied to increase the speed of detecting PQ events such as voltage sags, voltage surges, interruptions and harmonics. In Abdeslam et al. [72], the authors introduced a harmonic identification and compensation technique in that components can be selected individually, and the reactive power can also be compensated. In [59, 73–76], the authors employed an ANN to identify amplitudes and phase angles of distorted signals. The proposed algorithm achieved higher accuracy, convergence speed, and stability in permanent regimes than conventional methods used to identify harmonics. In Nascimento et al. [5, 77], an ANN was used to identify harmonic currents of a single-phase nonlinear load. The model could efficiently estimate the components present in the electrical system.

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In Flores-Garrido et al. [78], an ANN was developed to estimate the Fourier coefficients comparable to the fundamental voltage or current waveforms components. In Manito et al. [79], the authors presented a method to estimate the impacts caused by the harmonic voltage distortion of multiple non-linear loads in electrical grids. The work of Xu et al. [80] considered a solution by using an ANN based control method for a power electronic converter connected to an industrial microgrid. In [81], the authors detected islanding and PQ problems using ANN and Wavelet transform in a hybrid distributed generation system composed of PV and wind systems connected to PCC. In the work of Gong and Ruan [82], the authors presented a DNN framework addressing problems with low convergence speed, low accuracy, and poor generalization ability of electrical disturbance identification and classification methods. In addition, the authors developed a new disturbance identification method specific to a microgrid. Compared to other methods used for the same purpose, the proposed one presented high accuracy, higher convergence speed and strong generalization ability. In Kumar et al. [83], the authors used a model based on ANN to perform the prediction of electric charging systems. However, in [84–87], energy management strategies based on ANN were developed for PV systems, and the benefits of their use were presented. The study conducted by Ciabattoni et al. [88] dealt with the insertion of AI in the residential sector and the possibility of implementing demand-side flexibility. The AI-based approaches, when applied to estimation, prediction and aggregation of consumer energy consumption patterns, transform smart applications and the mode of consumption. They concluded that consumers would become more active in a short time, and their actions would significantly impact the energy sector. The study conducted by Çetin, Dalcalı and Temurta¸s [89] successfully estimated the parameters of the equivalent circuit of a squirrel cage induction motor through a feed-forward neural network. According to the results, using back-fed neural networks to predict equivalent circuit parameters of the squirrel cage induction motor is sufficient. DL and ML techniques are adequate for identifying PQ disturbances. This type of solution offers advantages such as low computational effort and simplification of the harmonics identification process. The following section discusses the concepts of ANN and their computational implementation.

6 DNN for Harmonic Estimation According to Silva et al. [90], the PQ assessment was significantly improved after employing the concept of intelligent systems. Araujo [91] states that the DNN can keep the information, identify and classify patterns, and perform predictions. Knowing that the identification of harmonics requires extracting the characteristics and peculiarities of the electrical signal, it becomes convenient to use DNN for this purpose, as shown in work done, for example, by Lima et al. [92–94].

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This section discusses the development of training, validation and testing samples and the implementation method of the proposed DNN. In addition, it performed comparisons between the proposed model and other consolidated regressors in the literature. The programming language used to obtain the information in this chapter is Python 3.8, through the development environment JupyterLab 3.0.14 [95].

6.1 Training, Validation and Test of the Samples The training of the DNN is performed from representative system samples that should represent the model. Thus, the samples containing waveforms of currents distorted by harmonics must be obtained. The defined aim is to estimate [96] amplitudes and phase angles of the harmonic components of the 3rd, 5th, 7th and 9th orders. The inputs to the computational model are fed with quarter-cycle waveforms in the time domain. The individual harmonic currents are generated and summed to form the current sample, such that ⎧ i 1 (t) = I1 cos(ωt + φ1 ) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ i 3 (t) = I3 cos(3ωt + φ3 ) i 5 (t) = I5 cos(5ωt + φ5 ) (10) ⎪ ⎪ ⎪ i 7 (t) = I7 cos(7ωt + φ7 ) ⎪ ⎪ ⎪ ⎩ i 9 (t) = I9 cos(9ωt + φ9 ) i(t) = i 1 (t) + i 3 (t) + i 5 (t) + i 7 (t) + i 9 (t)

(11)

where i n (t) is the harmonic current of order n in the time domain, In is the maximum amplitude of the harmonic current of order n in pu, φn is the phase angle in radians, ω is the angular velocity in radians per second (rad/s), and t is the time in seconds. The range of amplitudes and phase angles (Table 1) used in the sample set was defined based on the method described in [13] and the load currents described in [49, 97, 98]. The proposed DNN aims to estimate the harmonic orders 3rd, 5th, 7th and 9th, but it was also included in the samples the remaining odd and even harmonics up to the 13th order to represent the load currents observed in the literature. Similar waveforms in the sample set should be avoided to perform an optimized training process [99]. When the amplitude of the generated harmonics is 0 pu, only the phase angle 0 rad is used, ignoring the others. It is noteworthy that the computer resources used in this work (RAM of 16 GB and the CPU Intel(R) Core(TM) i710750H) limited the size of the dataset. However, it would be theoretically possible to synthesize a dataset with a more significant variation of amplitudes and phase angles.

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Table 1 Amplitudes and phase angles of the harmonics composed in the training and validation dataset Harmonic Amplitudes [pu] Phase angle [rad] 1 2 3 4 5 6 7 8 9 10 11 12 13

1.00 0; 0.025 0; 0.05; 0.10; ... ; 0.35 0; 0.025 0; 0.05; 0,10; ... ; 0.30 0; 0.025 0; 0.05; 0,10; ... ; 0.25 0; 0.025 0; 0.05; 0,10; 0,15; 0.20 0; 0.025 0; 0.025 0; 0.025 0; 0.025

0 0.1309 0; 0.0873; 0.1745; 0.2618 0.1309 0; 0.0873; 0.1745; 0.2618 0.1309 0; 0.0873; 0.1745; 0.2618 0.1309 0; 0.0873; 0.1745; 0.2618 0.1309 0.1309 0.1309 0.1309

The number of waveforms in the dataset is calculated by combining the number of permutations of amplitudes and phase angles for each harmonic signal and the number of sample positions in the quarter-cycle waveform, according to Table 1. The total size of the dataset is 10,692,864 quarter-cycle waveforms. The sampling rate defines the number of sample positions of a waveform and, consequently, the feature number of the dataset. According to the Implementation Guideline for Digital Interface for Transformation Instruments using IEC 61850-9-2 [100], the recommended sampling rate for a voltage with a fundamental frequency of 60 Hz is 15.36 kHz, which results in a signal discretized at 256 samplings per waveform, i.e., 64 samplings per quarter cycle. Thus, each dataset instance in this work comprises a vector with 64 sample positions. The dataset instances used for training, validation and testing of the DNN in this work were generated through an algorithm that generates a cosine signal, in the fundamental frequency, with an amplitude of 1 pu and phase angle of 0 rad (A2 , A3 , A4 , A5 , A6 , A7 , A8 , A9 , A10 , A11 , A12 , and A13 ) and sums signals of odd and even harmonic frequencies of orders 2 to 13 (φ2 , φ3 , φ4 , φ5 , φ6 , φ7 , φ8 , φ9 , φ10 , φ11 , φ12 , and φ13 ), as illustrated in Fig. 4a. The stage illustrated in Fig. 4b corresponds to the estimating process of the amplitudes (α3 , α5 , α7 , and α9 ) and phase angles (ϕ3 , ϕ5 , ϕ7 , and ϕ9 ) of raw data composed of a quarter-cycle waveform. It generated the set of signals of each harmonic order, which are summed together through a mathematics routine. After, a Z-score normalization was applied to the dataset, keeping the mean of each sample close to zero and the standard deviation close to the unit [101, 102]. With the dataset ready, setting up the DNN model can begin.

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Fig. 4 Procedures for data sampling and label generation, and the proposed DNN

6.2 DNN Model Architecture This work proposes an approach based on DNN for identifying amplitudes and phase angles of harmonics of 3rd, 5th, 7th and 9th orders. This aim does not involve the classification of waveforms into groups but getting a numerical value in each output close to the expected value of amplitudes and phase angles. Therefore, this work’s neural network performs the numerical values regression task. For DNN regression, the Adamax optimizer is convenient in applications involving a large amount of data and parameters, and it is computationally efficient [103]. Several loss functions and metrics are suitable for DNN regression; however, according to Chollet [104], the loss function and metric used in regression networks are, respectively, MSE and MAE, which can be defined as: MSE =



yv − y p

MAE = yv − y p

(12) (13)

where yv is the expected output and y p is the predicted output by the DNN. The MAE metric reports how close the estimates are to the expected values. The parameters of the input and output layers of the neural network can be determined, respectively, based on the shape of the dataset and the defined objectives. A 64-position vector represents each instance in the dataset. Therefore, the input layer of the model has a 64-position format. The output layer has 8 positions, or 8 neurons, given that each value will correspond to a value estimation of the amplitude and phase angle of the four harmonic frequencies analyzed in this work. The most used activation function (Table 2) to perform regression is the linear function [104]. The proposed ANN has layers fully connected, called dense layers. It is necessary to determine the number of layers and the number of neurons in each of these layers.

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Table 2 Recommended activation function according to problem type [104] Problem type Activation function Binary classification Multi-class and single-label classification Multi-class and multi-label classification Regression for arbitrary values Regression for values between 0 and 1

Sigmoid Softmax Sigmoid – Sigmoid

For this, the grid search method performs an exhaustive search on the parameter values of the ANN to return the best for the dataset and ANN model [102]. This method implements training and scoring for each model created within pre-established criteria. The grid search for cross-validation, available in the Scikit Learn [105], uses the k-fold cross-validation method (k-fold). This method provides training and testing indices to divide the sample set into training and testing. The method divides the set into 5 folds (k = 5), with each fold used as validation and the remaining folds forming the training set [106]. Besides k-fold, other cross-validation methods can be found in the literature like the Stratified k-fold, which is applied in neural nets for classification, and Stratified Random Split, which creates divisions preserving the same percentage for each target class of the dataset [107], used to divide instances into a training set and a test dataset, which are used, respectively, in neural net training and evaluation. The parameters aimed are the number of neurons (n) in the hidden layers and the number of hidden layers (c) with the tested values of n = {32, 40, 48, ..., 120, 128} and c = {2, 3, 4}. According to [104], the most widely used activation in DNN is ReLU, which is adopted in the hidden layers of this regression model. The grid search results were sorted regarding the MAE in the evaluation stage and the five best configurations, i.e., which with the lowest MAE presented in Table 3. The configuration with the lowest MAE was adopted to estimate amplitudes and phase angles of the harmonic components, as proposed in this work. The final model is described in Table 4. The DNN layers received the synaptic weights initializer called “Glorot Uniform” [108], which is the Keras default, with seed set (seed=333), so

Table 3 Results of the grid search Classification Hidden layers configuration 1 2 3 4 5

(120, 120, 120, 120) (80, 80, 80, 80) (128, 128, 128, 128) (88, 88, 88, 88) (32, 32, 32, 32)

MAE 0.030348 0.030856 0.031301 0.031316 0.031931

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Table 4 Best DNN configuration for the method Layer (type) Activation function Output format Input Hidden_1 (Dense) Hidden_2 (Dense) Hidden_3 (Dense) Hidden_4 (Dense) Output (Dense)

– ReLU ReLU ReLU ReLU Linear

(None, 64) (None, 120) (None, 120) (None, 120) (None, 120) (None, 8)

Number of parameters – 7,800 14,520 14,520 14,520 968

that the sequence of values initialized in the synaptic weights of the layers is always the same and enables a better reproducibility of this ANN. After defining the configuration of the DNN model, the next step is to perform the training, as presented in the Sect. 6.3.

6.3 Training of the Proposed DNN The sample dataset was separated into training and testing sets. Two separation methods were initially used: the random splitting method for training and testing and the stratified splitting method, which creates splits preserving the same percentage for each target group of the entire set. In both cases, 95% of the instances were destined for the training set and 5% for the test set. In the Keras interface, training is performed using the fit method [109]. Fifteen percent of the training samples were selected for validation while tuning the synaptic weights. The method was configured to perform the adjustment for 200 epochs and can be stopped by a stopping criterion when there is no minimum variation in the MAE of 0.0001 in 5 epochs in sequence. A learning rate scheduling function was established, the learning rate is initially 0.001 until the 10th epoch, and from this epoch on, it is updated according to the function presented in (14). lra = lrc · e−0,1

(14)

where lra is the updated learning rate, lrc is the current learning rate, and e is the Euler constant. The history, which is the record of training loss values and metric values at successive epochs, as well as validation loss values and validation metric values (if applicable), during training using random splitting can be seen in Fig. 5a, as well as the training history using stratified splitting can be seen in Fig. 5b. The training with random division occurred until epoch 54, when the stopping criterion was triggered to avoid unnecessary computational effort since the mean absolute error did not decrease at the specified rate of 0.0001 in 5 epochs.

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Fig. 5 Training history: a using random sample splitting and b with stratified sample splitting

The training with stratified division went until epoch 57, when the interruption by the stopping criterion occurred. In the last training epoch, the MAE was, respectively, 0.0190 and 0.0174. The evaluation with instances from the test set was performed with the two trained models. The model trained with randomly split samples reached a MAE of 0.0190; while the model trained with stratified split samples reached a MAE of 0.0174; values considered stabilized according to Fig. 5 and acceptable for the application [110]. The trained model with the samples divided in a stratified way was chosen from these results since the average errors were smaller. Evaluating only the amplitude estimate, the obtained MAE was 0.0051 pu, representing 29.3% of the mean absolute error of the training. On the other hand, the complete evaluation of phase angles found a mean error of 0.0296 rad, corresponding to 170.1% of the mean training error. This result may show that the DNN presents a better performance in the estimation of amplitudes when compared to the performance in the estimation of phase angles. Statistically, one can evaluate the performance of the model when subjected to the test samples by means of the box plot diagram in Fig. 6, which represents the grouping of individual errors, by harmonic order, observed in the output of the DNN model through of quartiles. In the diagrams of Fig. 6, the medians of all amplitude errors were less than 0.01 pu, and the medians of all phase angle errors were less than 0.05 rad. The 99% errors

Fig. 6 Box diagrams of the individual harmonic components from the DNN regarding the a amplitudes and b phase angles

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of the amplitude estimates of the 3rd, 5th, 7th, and 9th harmonics were smaller than 0.0296 pu, 0.0246 pu, 0.0188 pu, and 0.007 pu. The 99th percentile errors of the phase angle estimates of the 3rd, 5th, 7th, and 9th harmonics were smaller than 0.1599 rad, 0.1511 rad, 0.1065 rad, and 0.0368 rad. The 1% of the most significant estimation errors, represented by dashes in the diagram, is considered outliers.

6.4 Regression Methods Comparison Other methods well-known in the literature were tested and compared to those proposed to justify the application of DNN for harmonics estimation through regression. For the type of problem to be modeled in this work, the DNN performs a regression for arbitrary values. The main criterion for choosing these other methods was their ability to produce an output with multiple target values since the proposed DNN has this property. It is used the Ridge, kNN, DT, and RF methods, all available in the library scikit-learn [107]. The dataset used in the validation stage was extracted from 1% of the samples generated according to Sect. 6.1, totaling 106,928 waveforms. Grid search was used in all methods to determine the parameters required in each case. The determination coefficient (R 2 ) was used as a scoring criterion, such that  (yvi − y pi )2 2 R =1−  i (15) 1  i (yv − n i yvi ) where yv is the output label, y p is the prediction performed by the neural network, and n is the number of performed estimations. The best score is 1.0 [111]. The MAE was employed as a performance metric, so that the configuration with the lowest error was considered in the analysis result.

6.4.1

Ridge Regression

Ridge regression handles some problems of ordinary least squares by imposing a penalty on the size of the coefficients [107]. This method solves a regression model where the loss function is the linear least squares function, and the l2 norm gives the regularization [107]. It is also known as ridge regression or Tikhonov regularization. A parameter search was performed to determine a penalty (alpha) that provides the smallest MAE. Based on the classification achieved (Table 5), it was determined that the penalty would be 1.50. With the parameter set, the model was trained and run. The average difference between true and estimated amplitudes was 0.013398 pu. Between phase angles, the average error was 0.070651 rad.

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Table 5 Best parameters using grid search for the ridge regression Classification Alpha Score 1 2 3 4 5

6.4.2

1.50 1.75 2.00 2.25 2.50

0.596893 0.595492 0.594384 0.593478 0.592715

MAE 0.041797 0.041892 0.041969 0.042035 0.042092

k-Nearest Neighbors

The principle behind nearest-neighbor methods is to find a pre-defined number of training instances closest in the distance to the new point and predict the target feature using a majority voting mechanism from them [112]. The number of samples can be a user-defined constant (k-neighbor learning) or vary based on the local density of points (radius-based neighbor learning). The distance can be any metric measure: the standard Euclidean distance is the most common choice [105]. The neighbor-based regression can be used where the target feature is noncategoric. The value for the target feature assigned to a query point is calculated based on the average of the labels of its nearest neighbors [107]. Learning is implemented based on k-nearest neighbors of each query point, where k is an integer value specified by the user. The grid search algorithm was used to determine the number of neighbors (k) and the metric that provides the lowest MAE (Table 6). The best set of parameters is with the Euclidean metric and five neighbors when the MAE in identifying amplitudes was 0.013746 pu, and the MAE in phase angles was 0.083748 rad.

6.4.3

Decision Tree

The decision tree method is applied in non-parametric supervised learning for classification and regression. This method usually represents a table in tree format [113, 114]. The objective is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be viewed as a piecewise constant approximation [105]. Decision trees are used to fit a sinusoidal signal with an additional observation of noise. As a result, the model learns local linear regressions that approximate the sinusoidal signal [107]. The MAE was chosen as the criteria available in this method, the same used in the proposed DNN. The exhaustive parameter search algorithm was used to determine the maximum tree depth (max_depth) that provides the smallest MAE (Table 7). The best model from the grid search was with a maximum depth of 10. The identification MAE referring to amplitudes and phase angles are 0.020137 pu and 0.086628 rad, respectively.

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Table 6 Best parameters using grid search for the k-nearest neighbors Classification Distance function k Score 1 1 3 3 5 5 7 7 9 10 11 12 13 13 15

Euclidean Minkowski Euclidean Minkowski Minkowski Euclidean Minkowski Euclidean Manhattan Manhattan Manhattan Manhattan Euclidean Minkowski Manhattan

5 5 4 4 3 3 2 2 5 4 3 2 1 1 1

0.506460 0.506460 0.494220 0.494220 0.472730 0.472730 0.426658 0.426658 0.492669 0.479548 0.455360 0.405545 0.252745 0.252745 0.228932

Table 7 Best parameters using grid search for the decision tree Classification max_depth Score 1 2 3

6.4.4

10 20 30

0.445989 0.143038 0.093631

MAE 0.043637 0.043637 0.043654 0.043654 0.043767 0.043767 0.044112 0.044112 0.044459 0.044514 0.044685 0.045256 0.046507 0.046507 0.047716

MAE 0.048663 0.052913 0.053568

Random Forest

The random forest method comprises classifiers or regressors whose decision is made by considering individual decisions of a collection of decisions (named forest due to the collective of decision trees) [102, 113]. In random forests, each tree in the ensemble is built from a sample taken with replacement from the training set [107]. The function used as criteria in this method was also MAE for the same reason as the previous method. The grid search was performed around the maximum tree depth (max_depth) and the number of trees in the forest (n_estimators) and the highest score of such parameters presented in Table 8. A model with a maximum depth of 30 and a number of trees equal to 100 were the best parameters. The MAE from identifying amplitudes was 0.012640 pu, and from identifying phase angles were 0.075097 rad.

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Table 8 Best parameters using grid search for the random forest Classification max_depth n_estimators Score 1 2 3 4 5 6 7 8 9 10 11 12

6.4.5

30 30 20 20 30 20 20 30 10 10 10 10

100 50 100 50 20 20 10 10 100 50 20 10

0.557278 0.553282 0.556035 0.550981 0.540413 0.538808 0.514939 0.511628 0.504732 0.503401 0.502290 0.497629

MAE 0.043300 0.043367 0.043455 0.043621 0.043707 0.043932 0.044621 0.044666 0.047305 0.047352 0.047388 0.047511

Comparison of the Regression Methods

A DNN model was constructed with 4 hidden layers with ReLU activation function and 120 neurons in each hidden layer. The output layer has 8 neurons and a linear activation function. The dataset used in the previous regression methods is applied to the DNN model. This model obtained an average error in angle estimation of 0.011462 pu and 0.064639 radians regarding phase angles. The result, presented in Table 9 the dense network presented lower error in estimating amplitudes and phase angles. This result reinforces the ability of DNN to perform harmonic content estimation, which is also in line with the consulted literature (Sect. 5.1).

Table 9 Result comparison of the regressor methods Classification Method Execution [s] 1 2 3 4 5

DNN Ridge Random forest k-Nearest neighbor Decision tree

0.274272 0.001294 0.214537 0.377839 0.000554

MAE [pu] 0.039556 0.041797 0.043300 0.043637 0.048663

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6.5 Case Studies A study case is herein presented to demonstrate the operation performance of an APF devising selective harmonic compensation features. It is considered that the current references used to control the APF are synthesized by means of amplitude and phase angle setpoints obtained from the proposed DNN-based approach previously discussed in this chapter. Thus, the following simulation results demonstrate that the APF acts mitigating the current components from the 5th, 7th, 11th and 13th harmonic orders. The computational simulation developed for this study case considers an APF emulated by a three-phase and balanced controlled current source [115] (i.e., an ideal compensator), including the implementation of the DNN-based harmonic estimator, just as depicted in Fig. 7. Consequently, the compensation performance of the APF is mainly given by such an intelligent approach, since it is responsible for determining the harmonic content of the load currents, which is used to generate the current control references [9, 116, 117]. In Akagi et al. [32], it is discussed that the instantaneous active and reactive power theory (i.e., the PQ theory) can be used to synthesize reference currents to shunt APFs, while allowing them to satisfactorily compensate harmonics up to the control bandwidth limit [115]. Moreover, by adopting the PQ theory, one can precisely calculate the DC-link capacitance of the APF, given the inertial constant of its power electronics interface [4].

Fig. 7 A three-phase shunt APF synthesizing compensation currents based on the DNN-based selective harmonic estimation approach

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On the other hand, by designing the APF’s control system considering a DNNbased approach that estimates the load’s harmonic content, one has the advantage of selectively determining current components to be compensated, while achieving it at the speed of one quarter of the line frequency. Hence, for the following simulation results, the considered non-linear load is comprised of a controlled three-phase rectifier that feeds a balanced RL load. The firing angle of the rectifier semiconductors is π /6 rad (30◦ ) and two load scenarios are assessed during simulations. The first scenario presents R = 5 and L = 2 mH; and the second scenario presents R = 10 and L = 5 mH. It is also important to mention that the grid voltages are considered to be balanced and purely sinusoidal during simulations. The synthesis of the compensation currents for the APF starts by handling the load’s electrical quantities. First, the load’s currents are measured, being later conditioned and digitized, considering a low-pass filtering procedure capable of attenuating frequency components higher than 780 Hz (i.e., above the 13th harmonic order). The now digitized current quantizations support the expected harmonic estimation, which is performed by feature engineering processes and by adopting the DNN approach. The feature engineering allows to adjust the digital signals to comply with the input format required by the DNN method, which is attained by a normalization procedure and by the aggregation of 64 samples within an array at each quarter of the line frequency. The DNN method is then processed based on the DNN method, which returns the estimated harmonic amplitudes and phase angles of the input signals. Such estimated quantities can later be used to synthesize the APF’s control references, allowing a selective harmonic compensation feature to be devised. Thus, the compensation currents can be provided by the APF at the PCC, consequently improving the THD at this node. To give a quantitative representation of the compensation performance achieved by the DNN method, the computational simulation results are presented in Fig. 9 and Table 10. It can be clearly noted that all the quantities estimated by the DNN led to a significant compensation of the respective harmonic components, in some cases resulting in negligible distortions. Hence, the results show that the proposed DNN method is capable of estimating current harmonic components with satisfactory performance, allowing adequate compensation currents to be synthesized for the operation of the APF. In Fig. 8a, the PCC current is shown considering three simulation scenarios for the APF operation. Initially, between 0 and 37.5 ms, only the load current is depicted while the APF is disabled (see Fig. 8b as well). The compensation action of the APF starts at 37.5 ms, allowing it to inject the selected harmonic currents at the PCC, as seen in Fig. 8b. One can note in Fig. 8a that the PCC current now resembles a sinusoidal waveform, demonstrating that the harmonic distortions were significantly minimized. The third scenario then shows another perspective, in which the R L load is changed at 70.8 ms. Such a condition leads to a negative current demand step that requires the DNN to update its output in order to adequately estimate the new harmonic content targeted on the compensation. Note that the compensation provided by the APF maintained an effective operation upon the load step, although the injected currents were different from the previous scenario (i.e., see Fig. 8b).

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Fig. 8 a Input AC current of the non-linear load, and b the compensation current injected by the APF at the PCC. Only one phase is presented in this result

Fig. 9 Harmonic spectrum of the PCC current Table 10 Percentage of the individual harmonic distortions and THD at the PCC, before and after the APF operation Harmonics Before compensation (%) After compensation (%) 5 7 11 13 THD

22,3 11,7 9,1 6,6 29,9

3,1 2,3 0,5 0,8 12,2

An overall 57% minimization was achieved for the distortions as a result of the APF operation based on the DNN method since the THD reduced from 29,88% to 12,16%. In addition, the individual harmonic components (i.e., the ones selectively targeted during compensation) could be attenuated by 50 to 92%, indicating that the DNNbased approach applied to APFs presents satisfactory compensation performance (see Fig. 9). It is also important to mention that the remaining non-linear currents at the PCC are due to minor errors in the harmonic estimation process, also being a result of the higher order harmonic components not targeted on the selective compensation.

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7 Final Considerations The smart grid paradigm has grounds on the interdisciplinary utilization of multiple engineering technological tools. Among such modern tools lie the flexible management of active power conditioners, the implementation of control strategies striving for enhanced energy efficiency, as well as the integration of intelligent algorithms. Hence, this chapter demonstrated the methodological perspective and the application features of an approach that incorporates all these three aforementioned tools, as expected for the deployment of a real digitized power system (i.e., a smart grid). Therefore, it has been demonstrated that the application of the ANN-based technique applied to harmonic determination contributes to improving the performance of the SAPF and, by considering the context of smart grids, provides power quality improvement for a more efficient operation of the power system. All in all, the adoption of computational mechanisms capable of generating reference currents for APFs has significant relevance to the electrical power sector, mainly because artificial intelligence techniques are deemed to be omnipresent in smart grids. The integration of intelligent concepts, which in this chapter provides means to establish an efficient harmonic estimation strategy, assists the reader in acquiring knowledge of the fundamentals of power quality in modern grids. In addition, the technical content presented here supports the reader on expanding ones’ understanding on the application of automatic systems in the energy field. The results of the harmonic content estimation depicted in this chapter reinforce the multidisciplinary application perspective of the approach. At last, it highlights the trend of combining the electrical and computer engineering areas, striving for the implementation of smarter and more flexible power grids.

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Short Message Service System Applied in Predictive Control of Inverters Connected to the Electric Grid in Smart Grids Environments Angelo Lunardi, Alfeu J. Sguarezi Filho, Carlos E. Capovilla, and Ivan R. S. Casella Abstract This chapter presents a study of a system for remote control of active and reactive power injected into the electrical grid based on the Short Message Service (SMS) of a mobile telephone communication network. Wireless communication is widely used in SG and SMS has become a viable option due to its simplicity and low cost. There are numerous solutions for this application regarding the power control of the inverter of a Renewable Energy System (RES), however, this chapter will focus on the use of the Finite Control Set Model Predictive Control (FCS-MPC). In this type of control, the mathematical model of the inverter connected to the electrical grid is used so that it is possible to forecast the active and reactive power. In the optimization process, the cost function selects the switch states so that the controller reaches the desired active and reactive power references. These references are then sent via SMS by the SG operator to the RES under control. The results obtained by the proposed system were validated on an experimental bench.

1 Introduction Considered as an evolution of the classical power grids, Smart Grids (SGs) employ a modern bidirectional infrastructure for generation, transmission, distribution and energy consumption to increase efficiency, reliability and security of the complete power system. They are based on information and communication technologies, in A. Lunardi (B) · A. J. S. Filho · C. E. Capovilla · I. R. S. Casella Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André, SP, Brazil e-mail: [email protected] A. J. S. Filho e-mail: [email protected] C. E. Capovilla e-mail: [email protected] I. R. S. Casella e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_13

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addition to modern power elements, to carry out the monitoring and control tasks of the power grid [1, 2]. In Fig. 1, a typical SG infrastructure is presented [3]. Among the existing communication technologies, wireless technologies have stood out to integrate the communication infrastructure of SGs due to their high reliability, high flexibility, low cost, and easy installation and maintenance. However, the use of wireless technologies brings new challenges that require investigations to assess their performance and operational viability [4–6]. Considering the economic and technical aspects, including the service coverage area, the use of the Short Message Service (SMS) of the Second Generation (2G) Global System for Mobile Communication (GSM) standard can be an compelling option for control applications in SGs. As an interesting example, the SMS can remotely control thousands of Plug-in Electric Vehicle (PEV) chargers using the communication infrastructure of SGs [7]. In fact, a SG can employ the SMS to increase the performance of the power system, considering the appropriate time intervals for loading or unloading each PEV of the system [8]. On the other hand, when power is insufficient for current demand, a Smart Load Management (SLM) system can meet the customer’s emergency demands as shown in [9]. As an advantage, the SLM system allows the control and configuration of loads through SMS without the need for hardware or software modifications [10, 11].

Fig. 1 A typical SG infrastructure

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Fig. 2 Renewable energy grid with wireless control for SG applications

Advances in power electronics inverters have also contributed to the power system’s efficiency and robustness, which permits the link between renewable energy systems (RES) and the electrical grid [12]. The three-phase power converter is an ordinary solution that injects active and reactive power from the RES and the electrical grid. In this context, closed-loop control is required in this type of application [13, 14]. Regarding control, a technique that has aroused great interest recently is the Finite Control Set Model Predictive Control (FCS-MPC) [15]. This technique is designed considering future actions according to the plant model, and the minimized cost function selects the state of the converter in which the output reaches the desired references. FCS-MPC can be employed in inverters, which are responsible for converting electrical energy produced by photovoltaic modules or wind turbines to the power grid [16]. For instance, the robust FCS-MPC for rotor current control of DFIG is presented in [17]. In this context, this chapter’s main objective is to analyze the operation of a threephase inverter connected to the grid controlled by FCS-MPC in the scenario of an SG. Control system power references are sent remotely from the SG operation center to the RES by SMS over a realistic GSM communication network. Figure 2 shows the complete proposed FCS-MPC wireless control system.

2 Grid Connected Inverter and Direct Power Predictive Control 2.1 Stationary Coordinate System The equations described in the previous section consider the grid connected to a three-phase source by only three wires, without connection to the neutral point of the grid. This connection makes the three phases linearly dependent since: ua + ub + uc = 0

(1)

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In this way, it is possible to describe the three-phase system from two linearly independent vectors. A common way of describing the system is using the Clarke transform, or αβ0 transform [18]. This coordinate system has two perpendicular vectors that can be easily manipulated as a complex number with α being the real axis and β being the imaginary axis. Mathematically, the transformation is [19]: 2 (u a + k.u b + k2 .u c ) 3 √ 3 −1 +j k = e j2π/3 = 2 2 √ 3 −1 k2 = e j4π/3 = −j 2 2

aαβ =

(2)

(3)

(4)

where k represents spatial displacement by 120◦ for phase b and c. This transformation can be performed for voltages, currents, and fluxes, converting the threephase system to the stationary coordinate system. Matrix-wise, the Clarke transform is [20]: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ 2 −1 −1 uα ua 1 ⎣u β ⎦ = ⎣0 √3 √3 ⎦ . ⎣u b ⎦ (5) 3 3 3 u0 uc 1 1 1

2.2 The Mathematical Model of the Inverter Connected to the Power Grid The grid-connected converter is modeled according to the filter between the inverter and the grid. It can control the active and reactive powers supplied to the grid, with the implementation of control strategies such as Voltage Oriented Control (VOC) or predictive control, among others. For this, it is necessary to use the model in steady state, according to Eqs. (6), (7) and (8) [19]. vinv,a = Rg i g,a + L g

di g,a + vg,a dt

(6)

vinv,b = Rg i g,b + L g

di g,b + vg,b dt

(7)

vinv,c = Rg i g,c + L g

di g,c + vg,c dt

(8)

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where, vinv is the inverter voltage vector, i g is the grid current vector, vg the grid voltage vector. Rg and L g are the resistance and inductance of the grid filter.

2.3 Stationary frame Model of the Inverter Connected to the Power Grid The inverter connected to the grid was modeled using a source with variable voltage and frequency, filter and grid. In this work, the inductive filter was applied to the connection between the inverter and the voltage source. The model is presented in the Eq. (9). In this way, the power flow analysis becomes possible [21]. vinv,αβ = Rg ig,αβ + L g

dig,αβ + v g,αβ dt

(9)

Equation (9) can be decomposed in two components as (10) and (11): vinv,α = Rg i g,α + L g

di g,α + vg,α dt

(10)

vinv,β = Rg i g,β + L g

di g,β + vg,β dt

(11)

where v g,αβ and vinv,αβ expresses the voltage vectors of the power grid and the inverter, respectively. ig,αβ represents the current vector of the grid, Rg and L g express the resistance and the inductance of the inductive filter, respectively [22]. The active (P) and reactive (Q) power can be represented as: P=

3 e[v g · i∗g ] 2

(12)

Q=

3 m[vg · i∗g ] 2

(13)

The vector components of voltage and current can be represented by the components α and β. After performing the mathematical product and separating the real and imaginary portions, it obtained the following equations that express the active and reactive powers. 3 (14) P = [vg,α i g,α + vg,β i g,β ] 2 Q=

3 [vg,β i g,α − vg,α i g,β ] 2

(15)

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2.4 FCS-MPC FCS-MPC considers the predicted behavior of the system and the minimization of the cost function [15] to select the optimal state of the inverter. In this application, its objective is to directly control the elements of the apparent power (S = P + j Q) injected into the electrical grid. Therefore, the controller, based on the prediction of powers and references sent using the SMS interface by the SG operator, will select one of the vectors presented in Table 1 so that the powers are injected according to the references [23]. Figure 3 shows a block diagram of the inverter connected to the grid with FCSMPC, which summarizes the proposal. The vector form for the prediction of the current vector is presented in (16):   Ts Rg Ts p ig,αβ (k) + (v1 ( j) − v g,αβ (k)), ig,αβ (k + 1) = 1 − Lg Lg

Table 1 Switching states and voltage vectors of the inverter Sa Sb Sc Vector voltage vinv ( j) 0 1

0 0

0 0

vinv (1) = 0 vinv (2) = 23 Vdc

1

1

0

vinv (3) = 13 Vdc + j

0 0

1 1

0 1

vinv (4) = − 13 Vdc + vinv (5) = − 23 Vdc

0

0

1

vinv (6) = − 13 Vdc − j

1 1

0 1

1 1

vinv (7) = 13 Vdc − j vinv (8) = 0

Fig. 3 Finite control set diagram connected to the grid

√

3 3 √Vdc j 33 Vdc

√ 3 Vdc √ 3 3 3 Vdc

(16)

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and its components: p (k i g,α

p i g,β (k

  Ts Rg Ts i g,α (k) + + 1) = 1 − (v1 ( j) − vg,α (k)); Lg Lg

(17)

  Ts Rg Ts i g,β (k) + + 1) = 1 − (v1 ( j) − vg,β (k)). Lg Lg

(18)

To predict the apparent power components it is first necessary predict the currents injected into the electrical grid using the Eqs. (17) and (18) is employed for this task. The mentioned equation can be computed from the discrete representation of Eqs. (10) and (11) as per Euler method’s. Where: T s represents the sampling time. The prediction of apparent power elements P and Q is made after the current prediction, and it can be represented as follows: P p (k + 1) =

3 p p [vg,α (k + 1)i g,α (k + 1) + vg,β (k + 1)i g,β (k + 1)] 2

(19)

Q p (k + 1) =

3 p p [vg,β (k + 1)i g,α (k + 1) − vg,α (k + 1)i g,β (k + 1)] 2

(20)

For a short sampling time, vg (k + 1) ≈ vg (k) can be considered. Using the predicted power values to minimize cost function is presented in Eq. (21). g = |PRe f − P p (k + 1)| + |Q Re f − Q p (k + 1)|

(21)

PRe f and Q Re f are already demodulated references received via SMS by the ∗ ∗ control center, unlike PRe f and Q Re f which are the signals sent by the control center. Thus, the vectors presented in Table 1, which obtain the lowest value of g, are chosen and applied. Figure 4 shows a flowchart that explains how the algorithm works. Then a pseudo-code Algorithm 1 is presented to demonstrate the algorithm for implementing the FCS controller. This code makes it possible to understand the sequence structure of the operations necessary for the controller to work correctly.

3 Mobile and SMS Communication Infrastructure GSM is a 2G mobile telephony standard widely used around the world. Although mobile technologies are currently in the Fourth Generation (4G)/Fifth Generation (5G) [24, 25], GSM is still very attractive for low-rate applications (e.g. smart homes, credit card machines) due to its low cost and wide range [26, 27].

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Fig. 4 Flowchart for direct predictive power control

GSM employs Gaussian Minimum Shift Keying (GMSK) modulation and a combination of Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) to provide service to network users. Each GSM frequency channel occupies a 200 kHz frequency band and is divided into 8 time slots. Each user’s voice, data or SMS information is transmitted or received in specific time slots [10, 28]. To increase the robustness of the system to the degrading effects of the wireless communication channel and to guarantee the integrity of the transmitted information, a Forward Error Correction (FEC) system based on Convolutional Coding (CONV) is employed [28]. CONV has an excellent relationship between error correction capability and encoding and decoding complexity [29–31]. ∗ ∗ The proposed system used to remotely send active (PRe f ) and reactive (Q Re f ) power references is shown in Fig. 2. The references are transmitted by SMS messages

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while MPC - FCS active do The system variables as current, reactive and active power are measured gop = ∞ for j = 0 : 7 do v( j) = Switch States o f Vdc % Possible states of the converter Ts Rg Ts p ig,α (k) + (v1 ( j) − v g,α (k)) % System prediction of the ig,α (k + 1) = 1 − Lg Lg state j for the current   Ts Rg Ts p ig,β (k) + (v1 ( j) − v g,β (k)) % System prediction of the ig,β (k + 1) = 1 − Lg Lg state j for the current 3 p p P p (k + 1) = [vg,α (k)i g,α (k + 1) + vg,β (k)i g,β (k + 1)] % Active power prediction 2 3 p p Q p (k + 1) = [vg,β (k)i g,α (k + 1) − vg,α (k)i g,β (k + 1)] % Reactive power prediction 2 g( j) = |PRe f − P p (k + 1)| + |Q Re f − Q p (k + 1)| % Cost function calculation if g( j) < gop then gop = g( j) jop = j % Store the state with minor cost function end if if j = 7 then Apply v( jop ) Start again Else go to for loop again end if end for end while

Algorithm 1: MPC - FCS algorithm

from the SG operator to the RES. Each message contains up to 160 7-bit encoded characters and the transmission of the messages is performed on the Stand-alone Dedicated Control Channel (SDCCH). This channel ensures reliable delivery of power references information to the RES.

4 Analysis of Experimental Results Predictive control of the inverter for application in SG was implemented using a digital signal processor from Texas Instruments TMS320F28335, using electronic boards built in the laboratory for signal conditioning. The inverter used is a threephase Semikron inverter with six IGBT switches, with the system data are shown in Table 2. The power reference messages are transmitted by a SMS system developed under the OpenBTS project, which uses the GNU Radio software (version 3.7.10.1) [32, 33], to create a functional GSM network, as illustrated in Fig. 5.

368 Table 2 Parameters of grid Parameters Prated VR M S Lg C fo

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Value

Unit

3000 220 22 2.4 ≈16

[Wh] [V] [mH] [mF] [kHz]

Fig. 5 Communication system diagram

The GNU Radio software runs on a computer with Ubuntu operating system and connected to a BladeRF x115 Software Defined Radio (SDR) card, as shown in Fig. 5. To address legal issues, the OpenBTS GSM network was configured to use the Absolute Radio Frequency Channel Number (ARFCN) 975 (downlink frequency of 925.2 MHz), a valid GSM frequency in the ISM (Instrumentation, Scientific and Medical) band. The complete system diagram is shown in Fig. 6 and an image of the experimental bench built for system analysis is shown in Fig. 7. This infrastructure is placed in the control center of the grid operator. There the power references Pr∗e f and Q r∗e f are sent through SMS messages to the controller responsible for generators if transmitted the Pr∗e f or Q r∗e f references, it is informed at the beginning of the messages. Each SMS message consists of eight symbols, each symbol containing eight bits, altogether 256 possible different levels, where each level represents the power magnitudes in a 100 ms time interval. Figure 8 shows a complete SMS structure. Meanwhile, a script running on OpenBTS (open source project) version 4.0 creates SMS messages which contain the references of the active and reactive powers [10]. A sleep command is executed each time a message is sent, which results in a guard interval between messages. It can be concluded that the efficiency of this system is once a new message is sent only when the power value change, making the need for continuous transmissions unnecessary.

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Fig. 6 Complete wireless control system diagram

Differently from the transmission process, the receiving system is located in the RES connected to the electrical grid. The receiver system uses a PIC18F45K22 microcontroller development board, which is connected to a Teli G865QUAD GSM module with a cellular service operator card. During the reception process, the GSM signal is demodulated and decoded to recovery the transmitted power reference information. Three tests were applied to evaluate the proposed system presented in the flowing paragraphs. First test: a step input signal sent by the wireless reference channel of active power from 0 to 500 W which is repeated every 400 ms and the reactive power constant at zero. The test results are presented in Fig. 9, where it can be observed that the predictive controller achieves the references, and no peaks were associated with errors in the wireless channel. Figure 10 refers to the dynamic response of the grid voltage and current during this test. It was also observed that the current increases when the step occurs. Second test: As shown in Fig. 11, referrals were sent from the wireless channel containing a −300 to 300 var reactive power reference and maintaining the active power at 500W . The predictive controller reached the references, and the result in

370 Fig. 7 Experimental bench for analysis of the proposed wireless control system

Fig. 8 Structure of the SMS message

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Fig. 9 Experimental result of the response to the active power step

Fig. 10 Experimental result showing the current response to the active power step

Fig. 12 demonstrated the voltage and current behavior of the grid during the reactive transient along the test. Therefore, the current changes its phase. Three test: Fig. 13 present an additional test in which it was applied a constant 0 var reactive power reference, while the active power reference varied in 205 W, 506 W and 830 W, respectively. Again, the controller reached the reference, and the signal conditioning at the wireless channel output justifies the signal curves.

5 Conclusion An experimental analysis of control systems for power generation systems was presented in this chapter. It was based on obtaining the references of the grid operator through a GSM network (a standard wireless communication network). As observed in the test results, the FCS-MPC matches the expectations of the references received,

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PRef 00W

P

QRef

300var -300var

Q

Fig. 11 Experimental result of the response to the reactive power step

Fig. 12 Experimental result showing the current response to the reactive power step PRef P

QRef

506W

830W

245W

00var

Q

Fig. 13 Control response to a signal profile for active power

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and the performance of the control system was not affected by the noise introduced by the wireless communication system. the proposed system is validated by the results which are in accordance with control without wireless reference transmissions. The reliability and flexibility offer an exciting solution in power generation applications. The implementation offers a significantly reduced cost regarding implementing and operating a communication system for the smart-grid concept in power generation systems. Moreover, the proposed configuration can be used as a communication link for a more traditional implementation, implementing redundancy, aiming to improve system security. For Smart Grid systems, security and reliability in the signal transmission are essential for the system to work efficiently and with quality so that the energy delivered to the consumer is, in addition to being friendly to the environment, supplying energy for charging the requested energy demand. As an improvement to the system presented in the chapter, two points can be investigated an algorithm for a fixed switching frequency and a longer prediction horizon to optimize power converters’ operation and a new wireless communication protocol for a comparative protocols analysis.

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Wireless Sensor Network for Environmental Monitoring in Smart Cities Bruno Sousa Dias, Cicero Matheus da Silva Lacerda, Nailson Martins Dantas Landim, Humberto Xavier Araujo, and Starley do Nascimento Lobo

Abstract This chapter presents a wireless sensor network as a solution for environment monitoring in the context of smart grids in smart cities that combines the usage of two IoT solutions, one for measuring toxic gases, temperature, humidity, UV index and fire presence. and the other for monitoring the garbage collection by tracking the trucks and street sweepers carts movements to guarantee its coverage throughout the city. The two solutions shown effectiveness on obtaining this information dealing with internet connection oscillations, making the data available though a web application where the data can be exported. The tests not only proven its effectiveness as it shows a direction on a combined environmental monitoring that can address fire and environmental hazards using technology.

1 Introduction In smart grids systems, IoT (Internet of Things) devices with embedded sensors play a key role on electrical grids monitoring, and there are many other applications that can provide benefits for helping to maintain those systems, and one of these is environment monitoring on atmosphere gases, presence of smoke and toxic gases, B. S. Dias · C. M. da Silva Lacerda · N. M. D. Landim · H. X. Araujo (B) · S. do Nascimento Lobo UFT, Palmas, TO, Brazil e-mail: [email protected] B. S. Dias e-mail: [email protected] C. M. da Silva Lacerda e-mail: [email protected] N. M. D. Landim e-mail: [email protected] S. do Nascimento Lobo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 A. J. Sguarezi Filho et al. (eds.), Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications, Green Energy and Technology, https://doi.org/10.1007/978-3-031-37909-3_14

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temperature and humidity. With the mentioned monitoring is possible to detect fire in these areas and take security measures before the fire reaches the electrical grid. The smart grid can benefit from data coming in other areas of the city, making part of a smart city ecosystem, that leads to an increase on life quality and with a better usage of limited natural resources [1] this technological approach comes from growth and expansion of the internet that allows the creation of robust solutions to contemporary problems [2]. It’s in this context that the concept of smart cities emerges, that is: Cities that implements internet-based systems to maximize the population satisfaction, with the use of protocols and equipment to make it possible [3]. Here the focus is on the environmental scenario, where it is possible to have implementations both in the scope of monitoring and pollution reduction [4]. This work deals with environment monitoring considering the following variables: temperature, humidity, ultraviolet radiation, presence of fires, the concentration of toxic gases and smoke. And given this context it includes the monitoring of solid waste collection, with the collection of geolocation data of garbage responsible, making use of GPS (Global Positioning System) technology. On smart cities, location-based services working together with environment monitoring applied to the management of garbage collection routes, can have a fundamental role in the modernization of the public service leading not only to their improvement but ultimately impacting on social indicators. In Brazil, garbage management is the responsibility of municipal authorities. They must devise strategies to handle garbage disposal and educate its citizen and technicians to perform this essential task [5]. The development of tools that assist municipal authorities on city monitoring is important for the continuous improvement of provided services. This work proposes to an environmental monitoring platform for smart cities; with collection of environmental data, including the tracking of garbage collection vehicles, with a dedicated hardware connected to the internet, called cell, which can be present throughout the city, providing information on environmental conditions and waste disposal in real-time. Restricting geographically to Brazil, there is a plan to implement smart cities through the Digital Cities program, instituted in 2011 [6, 7].

2 Embedded System In this work, we will present two monitoring cell devices, one for collecting data related to environmental quantities, using the following sensors: DHT11 for temperature and humidity, Guva S12SD for ultraviolet radiation, MQ-135 for smoke and toxic gases and an infrared sensor for fire; The other cell will act monitoring solid waste collection by recording the route taken by its personnel, the Neo-6m GPS module will be used to obtain the geodesical coordinates, and finally, with the SIM800L 2G mobile network module will provided the needed internet connectivity.

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Fig. 1 ESP32 DevKit V1 development board

Each hardware prototype will be called a cell, where the environment cell will be responsible for collecting and recording the data obtained through the sensors and the tracking cell corresponds to the prototype used in the registration of the routes. Given the scope of the project, the microcontroller used will be the ESP32. This microcontroller was developed by Espressif Systems [8]. It’s designed for mobile applications, portable electronics, and IoT. It has WiFi and Bluetooth connectivity that are optimized for low power consumption during its operation [8]. To manufacture the prototypes, the ESP32 DevKit V1 development board will be used, which comes equipped with needed built-in circuits for programming and a micro-USB port as shown in Fig. 1. These cells communicate with the server-side web application through a RESTful API. REST (Representational State Transfer) is an architectural model focused on establishing restrictions on access to resources [9]. It is also worth noting another differential of the REST model: there are no sessions to store data between r The workflow on this architecture is the following: a cell intends to send the sensor data to the application. First, there is an endpoint capable of dealing with the data coming from the sensors. This has restrictions on how this data should be sent and only accepts it if it complies with the restrictions. Once the requirements are met, the cell will send the data to the resource locating it through a URI, and the server will return a response and close the connection. If the cell wants to use some other resource, it must request another connection to the server. As these requests are stateless, it can deal with intermittent connectivity. In Fig. 2 is possible to see that an example of how communication takes place. The cell sends a text that meets the criteria determined by the server-side application, it issues response and closes the connection. Note that the text sent in the example follows the JSON (JavaScript Object Notation) format, which is used to group information into keys and their respective values. A system whose architecture follows the REST standard is called RESTful [9]. Therefore, the server implements a RESTful architecture to receive the data. When a set of resources is created to work in a specific context, it is called an API (Application

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Fig. 2 REST application example

Programming Interface) [9]. Therefore, the server contains an API to communicate with the cell. In order to provide more security for this application, the connection will be made through a VPN (Virtual Private Network); that can be of these two types [10]: Clientto-Site and Site-to-Site. In this work, the first approach is used, which consists of a VPN server where users connect to it using credentials [10]. When the connection is established, that server becomes the new client gateway, where everything sent to it is encrypted as detailed below. In this work, StrongSwan is used, an Open-Source tool to serve VPNs. In accordance with [11], for this, it is necessary to have the VPN certificate, a private key for data encryption, and a range of private IPs to be assigned to clients that connect. As StrongSwan has tools that make easy these steps, the detailing will not be done here. With the connection established with the server, the data collected by the monitoring cells are stored in a database, so it is important that periodic backups are performed so that there is no loss of records. The backup consists of maintaining data redundancy for later recovery in case of loss of the original information, the backup procedures are usually done at night so that the backup does not interfere with other business hours procedures [12]. In this work, the open-source tool Bacula will be used. The first step is to establish that the storage will be done on the hard disk of the machine with Bacula installed. Once this is done, the FileSet to be copied is the /var/opt/mssql directory inside the container, which can be exposed to the outside world from a volume. Regarding the frequency, incremental backups are performed on working days and Saturdays. On Sundays, a differential backup takes place. On the first Sunday of each month, a full backup is made. Finally, it is worth mentioning that this process is carried out during the period in dawn, so as not to significantly impact the application server.

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3 Environmental Cell The working logic of this cell is: it checks if there is any monitoring scheduled. If it exists, it will take 20 readings from each sensor, calculate the average of each measurement, and send it to the server so that it can process and store the information. To build the prototype of this cell, the following components are used. The DHT 11 temperature and humidity sensor can measure temperatures from 20◦ to 60◦ C and humidity from 5% to 95% [13]. The Guva S12SD outputs the UV index (UVi). This index measures the intensity of ultraviolet radiation in an integer, to signal the necessary precautions, as listed in the ranges below [14]: • 1 and 2: No precautions. • 3 to 7: Avoid prolonged exposure to the sun. Preferably use sunscreen and look for shadows. • Above 8: Avoid any exposure to the sun. The MQ-135 sensor, manufactured by Hanwei Electronics, must be powered with 5V [15] and its resistance varies according to the concentration of measured gases. Considering that the ESP32 microcontroller works with 3.3V, it is necessary to include a voltage divider circuit for reading in this case. For fire detection, an infrared sensor is used, which will emit a digital signal that is true if there is a fire, otherwise, it will be false. All sensors presented are development versions, and sold with the needed circuitry for communication, the remaining step is adding some pull-up resistors and voltage dividers to calibrate the outputs. Figure 3 illustrates the schematic, the thicker lines are the +Vdc supply lines, and the dashed lines represent the connections of the GND terminals. The thinnest, continuous lines are the data connections. Finally, the resistors used are precision resistors with a resistance of 6.7 k . As it can be seen, there are only three resistors: a pull-up resistor for the DHT 11 and the other two to form a voltage divider for the MQ-135. The first tests with the prototype were carried out in closed and controlled places, which had the presence of other reference sensors. The two points chosen were a municipality in the state of Tocantins and the Federal University of Tocantins (UFT). Figures 4 and 5 illustrate the performed tests. After this step, the cell was tested in an open-area site inside the UFT. Finally, the performance of the equipment was monitored in the city. It is also worth noting that the reaction of the cell when disconnecting the connection to the network momentarily was tested. Figure 6 shows the debug messages for a cell. All the collected data is sent to the server and can be accessed by a web application. On the page, shown in Fig. 7, there is a green button that allows the scheduling of records, if necessary.

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Fig. 3 Cell schematic for environment, containing the microcontroller, sensors and resistors necessary for the construction of the circuit

Fig. 4 Test carried out with the environmental cell at the Federal University of Tocantins

It is also possible to export measurement data to a CSV (Comma Separated Values) file. For that, the Fig. 8 shows the form to be filled out, that has the possibility of choosing multiple environmental cells at once. Figure 9, in turn, shows the generated file.

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Fig. 5 Test carried out with the environmental cell in a municipality in the state of Tocantins, Brazil

Fig. 6 Debug Monitor

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Fig. 7 User interaction page

Fig. 8 Form to export *.csv file

Fig. 9 Exported *.csv file, contains raw data of measured magnitudes and can be used for research and analysis

As seen, the exported file has, in addition to the six measures, the following information: name, address, latitude, longitude, and the time the reading was performed. With this file, the user can whatever he needs, be it either a research or at a company.

3.1 Tracking Cell This cell collects geodesical data through a GPS module, sending it to an application to register the performed routes. It’s imperative to guarantee that the performed track is registered, for this reason, the system has two communications possibilities, the

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Fig. 10 Photovoltaic module

primary is mobile network, and the secondary is through Wi-Fi. So, in a failure on the primary channel, the data is logged to a memory card and once it reaches a known network it uploads the logged data. With this data the authorities can check their daily productivity and if the planned routes are effectively being performed to guarantee that the population is being properly served. Neo-6m GPS module is a low-cost easy to use option. It is an independent and high-performance, capable of parallel searches, finding the GPS satellites instantly [16]. The precision of the location is about 2,5 meters and it is limited only to speeds of 500 m/s (1800 km/h–1120 miles/h) and its maximum operation altitude is 50.000 meters [16]. As primary communication to the server, through a mobile network connection; the SIM800L is a GSM/mobile network module, capable of performing phone calls and SMS (Short Message System) messages, it’s main use is for Internet connectivity using mobile network. For this application, the internal 4MB ESP32 memory is not enough, for this reason it was added to 4GB SD memory card using the pins corresponding to the SPI (Serial Peripheral Interface) of ESP32 to store the registries when mobile network connection is not available. To supply the needed power, an 8V and 4400mah battery was used to guarantee long-lasting autonomy and to charge the battery, a small solar module was included, with nominal voltage and power of 12V and 1.5W respectively (Fig. 10). Whenever exposed to sunlight, the system will generate its energy, and consequently, it will reduce the need for human intervention in battery charging. The system power management is performed by the microcontroller with the circuit shown in Fig. 11. Where the current flow control and monitoring of the battery charge, is performed by the ESP32 itself through an ADC port which allows improvements through updates of firmware. This cell can receive power from an external 12V source. If the voltage at the solar module terminals is close to 12V, a relay will be activated and the external source

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Fig. 11 Tracking cell charging control circuit Fig. 12 Charging selection with a 12V relay

will be disconnected from the voltage regulator, as shown in Fig. 12. This allows the battery to be primarily charged through its own sustainable generation. Both load possibilities are connected to the LM7809 voltage regulator, which keeps the voltage stabilized at 9V. Figure 13 shows the schematic, where all microcontroller connections with load control circuit, SIM800L and Neo-6m modules and a memory card can be seen. In addition, an I2C display was also added, which will show the latitude, longitude, speed and battery level. Considering that this cell will be in motion and the number of components present, a printed circuit board was made, according to Fig. 14. The prototype in Fig. 15 was assembled using a printed circuit board, where the components were soldered and connected. For protection, it was used a sealed box, as seen on Fig. 16, with the solar cell being placed on its lid. A web application was developed as shown in Fig. 17, allowing users to access the registered data. To clearly show the routes was used the Google Maps API. Google Maps is a worldwide mapping service, where it is possible to visualize maps, satellite images and even traffic information. Using its API is possible to use this information on third-part web pages [17].

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Fig. 13 Schematic of the tracking cell, with load control circuit and other components Fig. 14 Printed circuit board design

4 Results 4.1 Environmental Cell Results The ambient cell performed as expected during the tests, consistently sends the readings to the application. When the internet connection was intentionally disrupted, the cell was able to reconnect. Figure 18 displays the information shown on the home screen. There are six graphs representing each of the six metrics: Temperature (Celsius degrees), Humidity (percentage), UV radiation (UVi), concentration of smoke and toxic gases (ppm) and fire (percentage of occurrences in relation to the number of

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Fig. 15 Developed Prototype of tracking cell Fig. 16 Developed Prototype of tracking cell in the sealed box

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Fig. 17 Integrated Google Maps page

Fig. 18 Average values of all measurements

measurements). Finally, each graph has three bars, one for the average of daytime values (orange), another for nighttime values (dark blue) and the third one encompasses the two shifts (green). In the section, shown in Fig. 19, there are three tabs available: Real Time, Time Series, and Mean Values, respectively. In each tab, it is possible to find the information related to the selected cell. In the first tab, there are six graphs again, one for each magnitude. However, this time, they are displayed in the form of speedometers and correspond to real-time

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Fig. 19 Real-time data per environmental cell

Fig. 20 Time series per cell

measurements, divided into two regions: a green one, indicating that the measurement is below the alert threshold, and another red, indicating that the measured value is alarming. for that cell concerning the magnitude. The black band only shows the division between zones and is located precisely at the threshold location. Moving forward through the tabs, Fig. 20 shows the Time Series screen, which plots measurements over a specific time range. Here it is also possible to choose both the environmental cell and the quantity to be displayed.

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Fig. 21 Average values per environmental cell

The last tab, Average Values, shows the averages of measurements of each quantity within the specified time range. Figure 21 shows the graphics, which are like the first section of the application.

4.2 Tracking Cell Results The tests took place in Paraiso do Tocantins—TO, Brazil. These tests involves verifying the data sent to the server, recording collected data in the database, backing it up, operating the web page, and integrating with Google Maps. The first test took place with fixed location, since there was no displacement, it is possible to observe that although the obtained points are not completely accurate, the variation is small, as can be seen, in Fig. 22. The next test was carried out on movement, sending the location points in real time to the server, with the connection provided by the SIM800L module, using a standard SIM card, with the route shown in Fig. 23. When returning to the origin point, between the Pouso Alegre and Jardim America sectors, the same avenue as the starting route was traveled. In Fig. 24, points are marked in both directions of 23 de Outubro Avenue. With this, the routes taken for the collection of solid waste are updated in real-time, making it possible to see which areas of the city have already been served on the day and if the itinerary is being fulfilled as expected. Note that it is possible to identify the route traveled by the obtained location points, however, in this case, the density of markings on the map can be improved with the use of a more modern mobile network module than the one used in this prototype.

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Fig. 22 Fixed location test—map view

Fig. 23 On Movement Test (Mobile Network connection)—map view

One test was performed without Internet connectivity, the points acquired were stored on the memory card, and each recorded location point generates a text file with latitude, longitude, speed, date, and time based on the data obtained by the Neo-6m module. At each loop the microcontroller checks if the internet connection with a mobile network or Wi-Fi is available, as soon as the connection is re-established, all the backup data will be sent to the server. This step is of paramount importance, as it prevents the routes taken from being lost due to a lack of internet connection. The same route taken in the previous tests was done but, the SIM card was removed from the SIM800L module, so that, there was no possibility of reconnection during

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Fig. 24 Location points on both sides of the avenue—map view

Fig. 25 On Movement Test (Backup)—map view

the test. Upon returning to the point of origin, the microcontroller identified a Wi-Fi network already registered and started sending data to the server automatically. With the location records plotted on the map in Fig. 25, the density of points obtained is considerably higher compared to those obtained with the real-time update by mobile network. With this test, the Backup system for the tracking cell was validated. As occurred in the test route with the mobile network, in this test, 23 de Outubro avenue was also traveled back and forth, the points obtained in both directions are shown in Fig. 26.

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Fig. 26 Location points on both sides of the avenue in the backup test

Fig. 27 On Movement Test (Wi-Fi)—map view

In another test on movement and real-time update, the connection was provided by a 4G mobile phone using a Wi-Fi tether, in this way, it is possible to compare the results obtained in real-time with the mobile network provided using the SIM800L module. The route taken is shown in Fig. 27.

5 Conclusion An automated real-time environmental monitoring is paramount for smart grids, especially in the context of smart cities, where the correct monitoring can prevent problems and give administrators solid information to make the city better managed and have a positive impact on its citizens lives.

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The system proposes a combined solution that effectively monitor the city environment through a sensor network that collect data that’s key for monitor and prevent fire near energy networks, through an IoT device and a RESTful web application. It also includes another IoT device that monitors garbage collection, which is key to city administration helping to maintain its environment. These connected sensors, called cells, as verified on performed tests were able to collect this real-world physical data and through the applications make it easily available for both administrators and companies, being able to export data that can be used for analysis or by other applications. The environmental cell shown on performed tests the ability to do this collection, with robustness especially on connection interruption, having a dedicated web dashboard, and a time series data collection. once deployed in scale it can serve to other uses besides the original proposition, and with the given time series collection can prove valuable for preventing damage on smart grids as well as other uses besides the scope of this project. In the tracking cell, the performed tests shown that the garbage trucks can be correctly accurately tracked, with intermittent connection, presenting a backup solution that guarantees that once the connection resumes it send the visited coordinates to the application. In this device it was observed that the latency on the network connection using the SIM800L module can affect the coordinates density as the requests to the API can take longer. The tests that were also performed without connection, relying on the backup or over Wi-Fi showed a bigger coordinates density. The tests were performed in a car, and the latency between these different connections could be noticed. Considering that street sweepers and garbage collection trucks move at low speed, it doesn’t have an impact on the overall use of this solution. In conclusion, real-time monitoring of environmental variables has the potential to improve energy security and management in smart grids. Its integration into the IoT ecosystem of a smart city can provide valuable data for controlling and managing the electrical network. For example, when the temperature of the entire city or specific areas is identified as elevated, it can serve as an alert for increased consumption due to the use of cooling systems such as air conditioning.

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