Wind Power Electric Systems: Modeling, Simulation, Control and Power Management Control (Green Energy and Technology) [2 ed.] 3031528824, 9783031528828

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Green Energy and Technology

Djamila Rekioua

Wind Power Electric Systems Modeling, Simulation, Control and Power Management Control Second Edition

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**.

Djamila Rekioua

Wind Power Electric Systems Modeling, Simulation, Control and Power Management Control Second Edition

Djamila Rekioua University of Bejaia Laboratoire LTII Bejaia, Algeria

ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-3-031-52882-8 ISBN 978-3-031-52883-5 (eBook) https://doi.org/10.1007/978-3-031-52883-5 1st edition: © Springer-Verlag London 2014 2nd edition: © 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.

Acknowledgements

In general, a book cannot be written without the support of others. I owe thanks to everyone who assisted me. I would like to express my gratitude to my colleagues and Ph.D. students in renewable energy and power electronics at the Laboratory LTII (University of Bejaia) for their collaboration and cooperation on many projects in renewable energy. To my family, my husband and my children. In addition, thanks are due to Springer-Verlag for publishing this book. Djamila Rekioua

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Introduction

The book aims to provide a comprehensive understanding of wind energy and wind energy conversion systems. Moreover, it covers not only basic definitions but also delves into advanced topics such as optimization, modeling, simulation, and various linear and nonlinear control strategies. Additionally, the primary audience is students in both undergraduate and postgraduate programs, especially those studying electrical engineering. The goal is to facilitate a quick grasp of concepts related to wind systems, including models, control techniques, and optimization methods. Furthermore, the book goes beyond theoretical concepts by providing practical applications using MATLAB/Simulink. In each section, mathematical models for different wind systems are presented. Also, readers are given corresponding examples for implementation using the MATLAB/Simulink package. The first part of the book addresses stand-alone wind applications, such as rural electrification and pumping. This indicates a practical approach. Moreover, it considers real-world scenarios where wind energy can be harnessed for specific purposes. Furthermore, the second part of the book explores applications in grid-connected systems. This reflects an awareness of the increasing integration of wind energy into existing power grids and the need for understanding the complexities of such systems. Also, the inclusion of examples under the DSPACE package suggests a focus on hardware-in-the-loop simulation and experimentation. This provides readers with a more hands-on experience in implementing the concepts discussed in the book. Moreover, the book introduces various electrical machine control approaches, including vector control, direct torque control, and fuzzy logic controllers. This reflects a comprehensive exploration of control strategies relevant to different types of drive systems associated with wind energy conversion. Additionally, intelligent techniques for optimizing wind operation are discussed. This indicates a recognition of the importance of optimizing the performance of wind energy systems for efficiency and reliability. Finally, by writing this book, the author aims to contribute to the existing knowledge in the field of wind energy. The emphasis on modeling, optimization, control strategies, and power management control suggests a desire to advance the understanding of these aspects within the wind energy domain.

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Introduction

Aims of the Book Many books currently on the market are treating on the wind energy and wind energy conversion systems. This book treats not only elementary definitions on wind energy but also optimization, modelization, simulation, and various linear and nonlinear controls applied to wind systems with applications under MATLAB/Simulink. The main objective of this book is to enable all students, both in graduation and postgraduation, especially those in the fields of electrical engineering, to quickly understand the concepts of wind systems, providing models, control, optimization strategies, and power management control. How the Book is Organized? The book is organized through ten chapters as follows: Chapter 1 of this book is dedicated to wind energy conversion systems that serve as a foundational resource for students and researchers aspiring to gain a comprehensive understanding of wind energy. The content of this chapter contains but not limited to essential aspects of wind energy systems, including operational principles, meteorological aspects, system configurations, pre-sizing considerations, maintenance strategies, and costs. In Chap. 2 of this work, the different complex parts of wind energy conversion systems and their principle of operation are represented and extensively discussed. Various wind turbine and generator technologies are employed for mechanical to electrical energy conversion. Moreover, the different generators used for variablespeed wind turbine systems are presented and compared in terms of advantages, drawbacks, power range, and applications. Deep and detailed modeling of common wind energy conversion is discussed in Chap. 3. Wind energy conversion modeling involves the mathematical and computational representation of various aspects of wind energy systems used to predict and analyze their performance. Furthermore, the different components of the WECS are modeled, and an application under Matlab/Simulink is made with some simulated results. Chapter 4 also explores the different converter topologies integrated with wind energy systems. Furthermore, their unique characteristics and applications are well highlighted. To support examination and simulation, the chapter offers MATLAB/ Simulink programs, a commonly utilized software platform for modeling, simulation, and the design of control systems. Moreover, the chapter points out the importance of control and optimization in guaranteeing the efficient and dependable operation of systems. This allows researchers and engineers to improve the design, operation, and overall performance of wind energy conversion systems. Chapter 5 thoroughly discusses the optimization of wind turbines with a focus on the various maximum power point tracking (MPPT) methods utilized in wind power systems. The advantages, drawbacks, and applications of each method are outlined in this chapter. The selection of the MPPT technique is influenced by factors such as system scale, wind speed range, control requirements, and desired accuracy. Additionally, simplicity in implementation characterizes classical methods like TSR and P&O, making them commonly utilized in small to medium-scale systems. In contrast,

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advanced intelligent artificial methods such as SMC, FLC, AFLC, PSO, ANN, WRBFM, and RBFM are characterized by enhanced control precision, robustness, and adaptability to complex systems. Improved efficiency, stability, and robustness across a wide range of wind power systems are offered by hybrid MPPT techniques that combine the advantages of different methods. In Chap. 6, various energy storage systems appropriate for diverse applications are presented, taking into consideration their specific properties and characteristics. Among these are pumped hydro storage, compressed air energy storage, electrochemical batteries, fuel cells, solar fuels, and thermal energy storage. The determination of an energy storage system is influenced by factors such as energy management requirements, power quality needs, and specific applications. Chapter 7 deals with the various nonlinear control strategies for wind turbine systems, focusing on mechanical and electrical aspects. Examples using Matlab/ Simulink demonstrate nonlinear control by static state feedback, nonlinear dynamic control by state feedback, and indirect speed control. The findings highlight the effectiveness of nonlinear dynamic control by state feedback in mitigating disturbances and achieving superior system performance. In Chap. 8, hybrid wind systems are examined, along with their configurations, combinations, and applications. The chapter showcases synoptic schemes and simulation applications, including a study on a hybrid wind/PV/Diesel system with battery storage. The results emphasize the potential of hybrid wind systems in various applications, such as electrification and pumping systems, providing insights for system design, optimization, and operational strategies. Chapter 9 offers wind turbine examples with simulation and experimental tests, serving as valuable resources for students studying electrical engineering. The examples provide a comprehensive understanding of wind turbine identification, simulation, modeling, control, and performance analysis. The combination of simulation and experimental tests bridges the gap between theory and practical application, facilitating student learning and skill development. Last but not least, the last chapter (Chap. 10) focuses on power management control strategies used in wind energy conversion systems. Different examples and case studies are presented with their advantages, drawbacks, and key factors. The optimal power management control configuration depends on factors such as grid connection requirements, control objectives, turbine size and type, wind speed and variability, energy storage systems, cost considerations, and environmental and grid integration requirements. Careful evaluation of these factors leads to the selection of an optimal power management control configuration for a specific wind energy conversion system.

Contents

1

Wind Turbine Applications Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 General Overview of Wind Turbine Characteristics . . . . . . . . . . . . 1.2.1 Site Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Rotational Axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Output Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Rotational Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.5 Method Using . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.6 Other Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Global Structure of a Conversion Wind System . . . . . . . . . . . . . . . 1.3.1 Wind Speeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Aerogenerator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 Autonomous and Grid Systems . . . . . . . . . . . . . . . . . . . . 1.3.5 Power Electronics Interface . . . . . . . . . . . . . . . . . . . . . . . 1.4 Introduction to Wind Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Standalone Wind Systems . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Direct-Coupled Wind System . . . . . . . . . . . . . . . . . . . . . . 1.5 Turbine Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Standalone Wind System with Storage . . . . . . . . . . . . . . . . . . . . . . 1.7 Hybrid System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Grid-Wind Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 Sizing of Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9.1 Determination of Load Profile . . . . . . . . . . . . . . . . . . . . . 1.9.2 Analysis of Wind Velocity . . . . . . . . . . . . . . . . . . . . . . . . 1.9.3 Calculation of Wind Energy . . . . . . . . . . . . . . . . . . . . . . . 1.9.4 Size of Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9.5 Size of Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.10 State-of-the-Art Developments in Wind Power . . . . . . . . . . . . . . . . 1.11 Maintenance of Wind Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12 Total Costs for Wind Turbine Installation . . . . . . . . . . . . . . . . . . . .

1 2 3 3 6 6 7 7 7 8 8 11 26 28 31 32 32 35 36 37 38 40 41 41 42 43 43 44 45 46 48 xi

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1.13 Onshore and Offshore Wind Power Technologies . . . . . . . . . . . . . 1.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

48 48 51

Electrical Generators Used in WECS . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Fixed-Speed Wind Turbine Systems . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Variable-Speed Wind Turbine System . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Squirrel-Cage Rotor Induction Generator . . . . . . . . . . . . 2.3.2 Dual-Stator Induction Machine (DSIM) . . . . . . . . . . . . . 2.3.3 Doubly-Fed Induction Generator (DFIG) . . . . . . . . . . . . 2.3.4 Self-cascaded Machine or Brushless Doubly-Fed Machine (BDFM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.5 Dual-Stator Winding Induction Machine . . . . . . . . . . . . 2.3.6 Doubly- Fed Induction Machine with Wound Rotor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.7 Synchronous Generator with External Field Excitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.8 Permanent Magnet Synchronous Generator . . . . . . . . . . 2.3.9 Other Electrical Generators Used . . . . . . . . . . . . . . . . . . 2.3.10 Comparison of the Different EGs Used in WECSs . . . . 2.4 EG Used in Autonomous Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Synchronous Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Power Electronics Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 55 57 57 58 58

3

Wind Energy Conversion Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Global Structure of WECS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Aerogenerator Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Dynamical Turbine Model . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Electrical Generators Modeling . . . . . . . . . . . . . . . . . . . . 3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

91 91 92 93 108 108 119 119

4

Power Electronics Used in WECS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Power Electronics Components in WECS . . . . . . . . . . . . . . . . . . . . 4.3 Power Electronics Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Soft Starter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Capacitor Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Diode Rectifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 The Back-to-Back PWM-VSI . . . . . . . . . . . . . . . . . . . . . 4.3.5 Modeling of the Rectifier . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.6 Modeling of the Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . .

121 121 122 124 125 125 125 125 127 129

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59 61 62 67 67 68 70 71 71 83 86 86

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4.3.7 Modeling of the Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.8 Tandem Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.9 Matrix Converter (MC) . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.10 Multilevel Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.11 DC/DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Other Converter Topologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Full-Bridge Converter (FBC) . . . . . . . . . . . . . . . . . . . . . . 4.5 Load Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Grid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Classification of Power Converters WECS Based on Voltage Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129 129 132 134 136 137 137 141 145

Optimization Methods Used in WECSs . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction to Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . 5.2 Maximum Power Point Tracking (MPPT) Algorithms . . . . . . . . . 5.2.1 Perturb and Observe (P&O) Technique or Hill Climb Searching (HCS) . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Tip Speed Ratio Method (TSR) . . . . . . . . . . . . . . . . . . . . 5.2.3 Power Signal Feedback (PSF) Method . . . . . . . . . . . . . . 5.3 Optimal Torque Control (OTC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Comparison of P&O, TSR and PSF . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Sliding Mode Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Fuzzy Logic Controller Technique . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Adaptative Fuzzy Logic Controller (AFLC) . . . . . . . . . . . . . . . . . . 5.8 Artificial Neural Networks (ANN) Method . . . . . . . . . . . . . . . . . . . 5.9 Radial Basis Function Network (RBFN) . . . . . . . . . . . . . . . . . . . . . 5.10 Particle Swarm Optimization (PSO) Method . . . . . . . . . . . . . . . . . 5.11 Adaptative Neuro-Fuzzy Inference System (ANFIS) . . . . . . . . . . . 5.12 Comparison Between Different Optimization Methods . . . . . . . . . 5.13 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153 153 153

Modeling of Storage Energy Systems Used in WECS . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Electrochemical Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Electrochemical Batteries . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Battery Electrochemical Model . . . . . . . . . . . . . . . . . . . . 6.3 Hydrogen Energy Storage (HES) . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Mechanical Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Flywheel Energy Storage (FES) . . . . . . . . . . . . . . . . . . . . 6.4.2 Pumped Hydro Energy Storage (PHES) . . . . . . . . . . . . . 6.4.3 Compressed Air Energy Storage (CAES) . . . . . . . . . . . . 6.5 Electromagnetic Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Supercapacitor Energy Storage (SES) . . . . . . . . . . . . . . .

185 185 185 185 190 192 195 195 206 207 208 208

147 151 151

153 157 160 163 168 168 172 174 176 177 178 180 181 182 183

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6.5.2

Superconducting Magnetic Energy Storage (SMES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Thermal Energy Storage (TES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

8

208 208 209 211

Overview of Control Methods Used in WECSs . . . . . . . . . . . . . . . . . . . 7.1 Basic Principles of Wind Turbine Control Systems . . . . . . . . . . . . 7.2 Level 1 (Mechanical Part) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 No Linear Control by Static State Feedback (NLCSSF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Nonlinear Dynamic Control by State Feedback (NLDCSF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Indirect Speed Control (ISV) . . . . . . . . . . . . . . . . . . . . . . 7.2.4 Comparison Between the Three Controls . . . . . . . . . . . . 7.3 Level 2 (Electrical Part) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Scalar Control of Wind System (SCWS) . . . . . . . . . . . . 7.3.2 Vector Control of Wind System (VCWS) . . . . . . . . . . . . 7.3.3 Direct Torque Control of Wind System (DTCWS) . . . . 7.3.4 Modulated Hysteresis Direct Torque Control of Wind System (MHDTCWS) . . . . . . . . . . . . . . . . . . . . 7.3.5 Direct Power Control of Wind System (DPCWS) . . . . . 7.3.6 Sliding Mode Control (SMC) . . . . . . . . . . . . . . . . . . . . . . 7.3.7 Fuzzy Logic Controller (FLC) . . . . . . . . . . . . . . . . . . . . . 7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

215 215 216

Hybrid Systems in Wind Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Advantages and Disadvantages of a Hybrid System . . . . . . . . . . . 8.2.1 Advantages of Hybrid System . . . . . . . . . . . . . . . . . . . . . 8.2.2 Disadvantages of a Hybrid System . . . . . . . . . . . . . . . . . 8.3 Configuration of Hybrid Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Architecture of DC Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Architecture of AC Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Architecture of DC/AC Bus . . . . . . . . . . . . . . . . . . . . . . . 8.3.4 Comparison of the Three Configurations . . . . . . . . . . . . 8.3.5 Classifications of Hybrid Energy Systems . . . . . . . . . . . 8.4 Different Combinations of Hybrid Systems . . . . . . . . . . . . . . . . . . 8.4.1 Hybrid Wind/Photovoltaic System . . . . . . . . . . . . . . . . . 8.4.2 Hybrid Wind/Photovoltaic/Diesel Generator System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.3 Hybrid Photovoltaic/Wind/Hydro System . . . . . . . . . . . 8.4.4 Hybrid Photovoltaic/Wind/Fuel Cell System . . . . . . . . . 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

249 249 249 249 250 250 251 251 251 254 254 256 256

216 218 219 223 225 225 227 228 233 239 240 244 245 246

261 267 269 270 271

Contents

9

Examples and Importance of Wind Systems . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Some Examples of Wind Turbines . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Wind Turbine of 600 W . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Importance of the Growing New Projects of WECSs in the World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Power Management Control of Wind Energy Conversion Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Advantages and Drawbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Examples of Some PMC of Wind Systems . . . . . . . . . . . . . . . . . . . 10.3.1 Wind/Battery System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 Wind/PV/Battery System . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.3 Wind/Diesel/Battery System . . . . . . . . . . . . . . . . . . . . . . 10.3.4 Wind/Hydrogen/Battery System . . . . . . . . . . . . . . . . . . . 10.3.5 Wind Power Generation System with Compressed Air Energy Storage . . . . . . . . . . . . . . . . . . . 10.3.6 Wind/Hydroelectric/Battery System . . . . . . . . . . . . . . . . 10.3.7 Wind/Flywheel System . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.8 Wind/Supercapacitor Energy Storage . . . . . . . . . . . . . . . 10.3.9 WTb/Battery/Flywheel . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.10 WTb/Battery/Diesel Generator . . . . . . . . . . . . . . . . . . . . 10.3.11 Wind Turbine/Battery/Supercapacities . . . . . . . . . . . . . . 10.3.12 WTb/Battery/Fuel Cells . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

273 273 273 273 283 294 295 295 296 297 297 297 300 301 303 303 304 307 308 310 311 312 315 315

Notations

General v, V i, I

Voltage in instantaneous and vector notation Current in instantaneous and vector notation

Subscripts d, q α, β s, r a, b, c

Quantities in d-axis and q-axis Quantities in α-axis and β-axis Stator and rotor Quantities in phases a, b, and c

Superscripts AC AFLC ANN ANFIS BESS BN DFIG BDFIG BDFM BP CAES CIEMAT

Alternative Current Adaptive Fuzzy Logic Controller Artificial Neural Networks Adaptive Neuro-Fuzzy Inference System Battery Energy Storage System Big Negative Doubly-Fed Induction Generator Brushless Doubly Fed Induction Generator Brushless Doubly Fed Machine Big Positive Compressed Air Energy Storage Centro De Investigaciones Energéticas, Medioambientales Y Tecnológicas xvii

xviii

CSC CPG DC DFIM DOD DPC DPCWS DSIM DSWIM DTC EG EMS ES ESS Eles ELC Elmes FBS FES FLA FLC FOC FSWT FT G GSC GTO HAWT HCS HES HPS H2 ICS IGBT GBF LIG MHDTC MN MP MS NN LAS Li-Ion LIS MPPT

Notations

Current Source Converter Claw-Pole Generator Direct Current Double-Fed Induction Machine Depth of Discharge Direct Power Control Direct Power Control of Wind System Dual Stator Induction Machine Dual Stator Winding Induction Machine Direct Torque Control Electrical Generator Energy Management System Energy Storage Energy Storage Systems Electrical Energy Storage Electronic Load Controllers Electromagnetic Energy Storage Flow Batteries Storage Flywheel Energy Storage Flooded Lead Acid Fuzzy Logic Controller Field Oriented Control Fixed Speed Wind Turbine Factor Temperature Gearbox Grid-Side Converter Turn-Off Thyristor Horizontal-Axis Wind Turbine Hill-Climb Searching Hydrogen Energy Storage Hybrid Power Systems Hydrogen Indirect Control Speed Insulated Gate Bipolar Transistor Gravity-Based Foundation Linear Induction Generator Modulated Hysteresis Direct Torque Control Means Negative Means Positive Mechanical Storage Neural Network Lead-Acid Storage Lithium Ion Lithium Ion Storage Maximum Power Point Tracking

Notations

Nas Nass NCS Nicd Ni-Zn OTC O2 PCU PE PHES PI PMC PMSG PMTFM P&O PSF PSBS RBFN PWM SAWS SC SCADA SES SCIG SCWS SG SIV SMC SMES SN SOC SP SRG STATCOM TC TCR TSC TES TLP TSR Tsos TSC VAWT VC VCWS

xix

Sodium-Sulfur Sodium-Sulfur Storage Nickel-Cadmium Storage Nickel-Cadmium Nickel-Zinc Optimal Torque Control Oxygen Power Control Unit Power Electronic Pumped Hydro Energy Storage Proportional Integral Power Management Control Permanent Magnet Synchronous Generator Permanent Magnet Transverse-Flux Machines Perturb & Observe Power Signal Feedback Polysulphide Bromide Storage Radial Basis Function Network Pulse Width Modulation Stand-Alone Wind System Scalar Control Supervisory Control and Data Acquisition Super capacitor Energy Storage Squirrel Cage Induction Generator Scalar Control Of Wind System Synchronous Generator Speed Indirect Control Sliding Mode Control Superconducting Magnetic Energy Storage Small Negative State Of Charge Small Positive Switched Reluctance Generator Static Synchronous Condenser Torque Coefficient Thyristor Controlled Reactor Turbine-Side Converter Termal Energy Storage Tension Leg Platform Tip Speed Ratio Transmission System Operators Thyristor Switched Capacitor Vertical-Axis Wind Turbine Vector Control Vector Control of Wind System

xx

VR VRB VRLA VSI VSWT WECS Z ZEBRA Znbr

Notations

Vanadium Redox Vanadium-Redox Flow Battery Valve-Regulated Lead Acid Voltage Source Inverter Variable Speed Wind Turbine Wind Energy Conversion System Zero Zero Emission Battery Research Activity Zinc Bromine

Chapter 1

Wind Turbine Applications Overview

This chapter serves as a valuable introduction and foundational resource for readers interested in wind energy conversion systems. The chapter’s primary goal is to provide readers with a fundamental understanding of wind energy conversion systems. This understanding includes the key principles and components involved in harnessing wind energy for electricity generation. Readers can expect to gain insights into the operational principles of wind energy conversion systems. This includes how wind turbines capture wind energy, convert it into mechanical energy, and ultimately generate electricity. Wind energy is heavily influenced by meteorological factors. The chapter likely covers meteorological aspects that impact wind energy, such as wind patterns, wind speeds, and seasonal variations. When planning wind energy projects, it’s important to consider factors such as site selection, turbine capacity, and grid integration. The chapter may provide guidance on pre-sizing considerations to optimize system performance. Wind turbines require ongoing maintenance to ensure reliable and efficient operation. Readers can expect to learn about common maintenance practices and the importance of regular inspections and repairs. The financial aspects of wind energy are crucial. The chapter may address the costs associated with wind energy projects, including initial investment, operational costs, and potential returns on investment. The chapter can also explore the broader implications of wind energy in the context of renewable energy production and its contribution to mitigating climate change and reducing greenhouse gas emissions. In summary, this chapter is a valuable resource for individuals interested in wind energy technology. It serves as a starting point for those who want to delve deeper into the field of renewable energy and sustainable electricity generation.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 D. Rekioua, Wind Power Electric Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-031-52883-5_1

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1 Wind Turbine Applications Overview

1.1 Introduction Wind energy continues to play a vital role in the transition to clean and sustainable energy sources, helping reduce carbon emissions and address climate change. Its flexibility, scalability, and ability to integrate with other energy sources make it a valuable component of the global energy landscape (Fig. 1.1). Wind energy is a renewable and environmentally friendly power source. It doesn’t produce harmful emissions, making it a clean and sustainable option for generating electricity. WECs operate independently, relying solely on the power of the wind. They don’t require associated fuel costs, which can lead to cost savings over time. Wind energy systems can be integrated with other power sources, such as fossil fuels or solar, to enhance overall system reliability and resilience. This combination of energy sources is known as hybrid power generation. Wind turbines are flexible in terms of installation and upgrades. More turbines can be added to a wind farm as power demand grows, allowing for scalable and adaptable power generation. The emergence of modern wind turbines began in 1979, with early models featuring power capacities ranging from 10 to 30 kW. These early developments laid the foundation for the growth of the wind energy industry. In contemporary times, new WEC projects primarily use wind turbines with significantly higher capacities, typically ranging from 2 to 5 MW. This increase in capacity has resulted in more efficient and costeffective wind power generation. Wind power technologies are classified based on the axis of the wind turbine, with horizontal-axis and vertical-axis configurations being the most common. They are also categorized by deployment location, which

Fig. 1.1 Advantages, historical development, and the various technology classifications of WECs

1.2 General Overview of Wind Turbine Characteristics

3

includes onshore (land-based) and offshore (sea-based) wind farms. Offshore wind farms have gained prominence in recent years due to their potential for higher wind speeds and larger scale projects.

1.2 General Overview of Wind Turbine Characteristics To provide a general understanding of wind turbine technologies and their applications, we give a general overview of wind turbine characteristics based on the following specified parameters (Table 1.1).

1.2.1 Site Installation 1.2.1.1

Onshore

Wind turbines installed on land, typically in open areas or rural locations. There are two common types of onshore wind turbine foundations: a. Spread Foundations: • Mat Foundation: Also known as a “raft” foundation, it is a large, flat, reinforced concrete slab that distributes the weight of the wind turbine evenly over a wide area. It is suitable for sites with relatively stable and strong soil conditions. • Strip Footing: This type of foundation consists of a long, narrow strip of reinforced concrete that runs along the length of the wind turbine tower. It is suitable for sites with cohesive soil that can support the load. b. Piled Foundations: • Driven Piles: Steel piles are driven into the ground using impact hammers to create a stable foundation. The piles can be driven vertically or at an angle, depending on the soil conditions. This type of foundation is commonly used in areas with variable soil conditions. • Augered Piles: Large-diameter holes are drilled into the ground, and the piles are inserted and then filled with concrete. This type of foundation is suitable for sites with loose or sandy soil. • Screw Piles: Also known as helical piles, these are large metal screws that are twisted into the ground using specialized machinery. They provide a stable foundation in a variety of soil conditions.

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Table 1.1 Overview of wind turbine characteristics based on specified parameters Wind turbine

Site installation

Onshore

Spread foundations

Mat/foundation slab Gravity foundation

Piled foundations

Driven piles Augered piles Screw piles

Nearshore

Fixed-bottom

Fixed-bottom monopile Fixed-bottom jacket

Floating

Floating platform Tension leg platform (TLP):

Hybrid

Nearshore wind farms with offshore extensions Combined wind and wave energy systems

Offshore

Floated substructure

TLP Barge Spar-Buoy Semi-submersible

Fixed substructure

jacket Monopile tripod gravity

Rotational axis

HAWT VAWT

Savonius Darius H-rotor

Output power

Large (several 100 kW to several mW) Medium (tens of kW to several 100 kW) Small (few kW to tens of kW)

Rotational speed

FSWT VSWT

Method using

Standalone system Wind farm Hybrid system

Other parameters

Tower height (continued)

1.2 General Overview of Wind Turbine Characteristics

5

Table 1.1 (continued) Hub height Blade length Rotor diameter Water depth

1.2.1.2

Nearshore

Wind turbines installed close to the shore or in shallow waters near the coastline. We provide some common types of nearshore wind turbine installations: a. Fixed-Bottom Nearshore Wind Turbines: • Fixed-Bottom Monopile: These turbines are installed on a single large cylindrical steel pile driven into the seabed, providing a stable foundation. • Fixed-Bottom Jacket: The turbines are supported by a steel lattice structure with multiple legs, which is fixed to the seabed. b. Floating Nearshore Wind Turbines: • Floating Platform: These turbines are mounted on floating structures that are anchored to the seabed using mooring lines. They can adapt to different water depths and are suitable for locations with challenging seabed conditions. • Tension Leg Platform (TLP): This type of floating turbine uses tensioned vertical tethers to provide stability, and the platform is held in place by buoyancy and tension. c. Hybrid Nearshore Wind Turbines: • Nearshore Wind Farms with Offshore Extensions: In this configuration, a wind farm is partially located near the shore and extends into offshore areas, combining the advantages of both onshore and offshore installations. • Combined Wind and Wave Energy Systems: Nearshore locations with wave energy potential can be utilized for hybrid installations that capture both wind and wave energy. 1.2.1.3

Offshore

Wind turbines installed in deeper waters, further away from the shore. There are two common types of offshore wind turbine substructures: 1. Floated Substructure • Floating Platform: This type of substructure is designed to float on the water surface and is anchored to the seabed using mooring lines. It is suitable for deep-water locations where fixed foundations are not feasible. Floating platforms can be further classified into different types, such as spar buoy, tension

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1 Wind Turbine Applications Overview

leg platform (TLP), semi-submersible, and floating offshore wind turbines (FOWTs). 2. Fixed Substructure • Monopile: A monopile is a large, cylindrical steel pile that is driven into the seabed to provide support for the wind turbine tower. It is suitable for shallow water depths and relatively stable seabed conditions. • Jacket Structure: A jacket structure consists of multiple steel tubular members connected together to form a lattice-like framework. It is suitable for deeper water depths and less stable seabed conditions. • Tripod Structure: A tripod structure is similar to a jacket structure but has three legs instead of four. It provides stability and can be used in various water depths. • Gravity-Based Foundation (GBF): GBF is a large concrete structure that is placed on the seabed and relies on its weight to provide stability. It is suitable for various water depths and can be pre-fabricated onshore and towed to the installation site.

1.2.2 Rotational Axis • Horizontal-Axis Wind Turbine (HAWT): The rotor axis is horizontal, and the blades rotate like a propeller. • Vertical-Axis Wind Turbine (VAWT): The rotor axis is vertical, and the blades rotate around a vertical shaft.

1.2.3 Output Power • Large: Wind turbines with high power output typically used for utility-scale applications, ranging from several hundred kilowatts to several megawatts. • Medium: Wind turbines with moderate power output suitable for commercial or small-scale applications, typically ranging from tens of kilowatts to several hundred kilowatts. • Small: Wind turbines with lower power output designed for residential or smallscale applications, usually ranging from a few kilowatts to tens of kilowatts.

1.2 General Overview of Wind Turbine Characteristics

7

1.2.4 Rotational Speed • Fixed-Speed Wind Turbine (FSWT): The rotational speed of the rotor is fixed and synchronized with the grid frequency. • Variable-Speed Wind Turbine (VSWT): The rotational speed of the rotor can vary to optimize energy capture and improve efficiency.

1.2.5 Method Using • Standalone System: Wind turbines used as independent power sources for off-grid or remote locations, providing electricity to local loads or standalone applications. • Wind Farm: Multiple wind turbines grouped together to form a wind power plant, typically connected to the electrical grid to supply electricity to a larger area. • Hybrid System: Integration of wind turbines with other renewable energy sources (such as solar or battery storage) or conventional power sources to create a hybrid power system for improved reliability and efficiency.

1.2.6 Other Parameters • Tower Height: The height of the wind turbine tower, which can vary depending on the site conditions and turbine size. • Blade Length: The length of the wind turbine blades, which affects the swept area and the amount of wind energy captured. • Hub Height: The height at which the blades are connected to the wind turbine hub, influencing wind capture and overall performance. • Floating or Fixed Foundation: The type of foundation used for offshore wind turbines, with options including fixed structures anchored to the seabed or floating platforms. • Rotor Diameter: The diameter of the wind turbine rotor, impacting the swept area and the amount of wind energy harvested. • Water Depth: Relevant for offshore wind turbines, referring to the depth of the water where the turbines are installed. • Platform: The structure that supports the offshore wind turbine, including substructures such as monopiles, jackets, or floating platforms.

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1 Wind Turbine Applications Overview

1.3 Global Structure of a Conversion Wind System The most basic configuration of a wind energy conversion system (WCES) is depicted in Fig. 1.2. The primary components of the WCES are the aerogenerator and the power electronics interface. The wind turbine plays a crucial role in converting mechanical energy into electrical energy through an electrical generator, while power electronic converters are responsible for controlling the aerogenerator. The rotor, consisting of blades, efficiently transforms wind energy into mechanical energy. As the rotor rotates at relatively low speeds, a gearbox is employed to increase the speed to the level required by the electrical generators. However, some smaller scale turbines utilize a direct-drive system, eliminating the need for a gearbox.

1.3.1 Wind Speeds Wind speed, also known as wind velocity, serves as the fundamental parameter in a wind energy conversion system. It is directly influenced by meteorological conditions and is typically measured using an anemometer or a weather station, with units expressed in meters per second (m/s) or kilometers per hour (km/h). In a laboratory setting, wind speeds can be measured using specialized equipment designed for controlled experiments. In order to capture and analyze wind speed characteristics, it can be effectively modeled within the MATLAB/Simulink environment, as depicted in Fig. 1.3. The obtained profile is shown in Fig. 1.4.

Fig. 1.2 Structure of a conversion wind system. With: R the blade’s radius, Vwind the wind speed or air velocity, ωt the turbine speed, G represents the gearbox, EG the electrical generator, and PE power electronics

1.3 Global Structure of a Conversion Wind System

9

Fig. 1.3 Wind variations under MATLAB/Simulink

Fig. 1.4 Wind speed profile obtained under MATLAB/ Simulink

1.3.1.1

Wind Resource Assessment

Wind resource assessment describes to the process of evaluating and analyzing the wind characteristics at a particular location to determine the energy potential obtained from the wind. It involves collecting and analyzing wind data, researching wind patterns, and estimating the wind resource parameters that influence the performance of wind energy systems. It is a crucial step in wind energy development, providing insights into the available energy potential and aiding in the planning and design of wind projects. Table 1.2 summarizes key aspects and methods used in wind resource assessment.

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Table 1.2 Aspects and description methods used in wind resource assessment Aspect

Description

Wind data collection

Gathering wind data from meteorological stations, remote sensing devices, or onsite measurements. Historical wind data can also be obtained from databases or reanalysis models

Wind speed distribution

Analyzing the statistical distribution Weibull distribution of wind speeds at a particular Rayleigh distribution location Probability density functions

Wind rose analysis

Examining the frequency and direction of wind occurrences to determine prevailing wind directions and identify potential wind resource variability. Wind roses display the frequency of wind speeds and directions in a graphical format

Wind power density

Calculating the wind power density, which represents the energy available in the wind at a specific location

Turbulence intensity

Assessing the level of turbulence in the wind, which affects wind turbine performance and structural loads

Wind shear analysis

Studying the variation in wind speed with height above ground level. Wind shear affects the performance and loads on wind turbines. Analyzing shear profiles helps in selecting appropriate turbine heights and understanding power production patterns

Wind farm layout analysis Evaluating the optimal arrangement of wind turbines within a wind farm to minimize wake effects and maximize energy production Uncertainty analysis

1.3.1.2

Assessing the uncertainties associated with wind resource assessment, including the accuracy of measurement devices, data quality, statistical modeling, and other factors. Conducting sensitivity analysis to understand the impact of uncertainties on energy yield predictions

Power or Performance Curve

It represents the relationship between the wind speed and the power output of the turbine. The power curve provides important information about the turbine’s performance and efficiency across a range of wind speeds. The power curve follows a characteristic shape. Initially, at low wind speeds, the power output is minimal or even zero until the cut-in wind speed is reached. As the wind speed increases, the power output climbs, and reaches its maximum at the rated wind speed. Beyond the rated wind speed, the power output may remain constant or start decreasing slightly. Finally, as the wind speed approaches the cut-out wind speed, the power output decreases significantly, and the turbine turn off for safety.

1.3 Global Structure of a Conversion Wind System Cut-in speed

Rated or nominal speed

11 Cut-out speed

1000

Power (W)

800 600 400 200 0 Wind speed (m/s)

Fig. 1.5 Typical curve for a wind turbine

In Fig. 1.5, we have defined the following wind speeds and their corresponding significance: • Cut-in wind speed (approximately 3.5 m/s): This is the minimum wind speed at which the turbine starts generating power. • Nominal wind speed or rated wind speed (ranging from 11 to 17 m/s): Within this range, the power output of the wind turbine reaches its maximum capacity as determined by the electrical generator. • Cut-out wind speed (ranging from 17 to 30 m/s): As wind speeds escalate, there is an increased risk of rotor damage, necessitating the activation of a braking system to halt the rotation of the rotor.

1.3.2 Aerogenerator 1.3.2.1

Wind Turbine Axis

Wind turbines are capable of rotating on either a horizontal axis or a vertical axis. Horizontal-axis wind turbines (HAWT) and vertical-axis wind turbines (VAWT) such as the Darius and Savonius designs are two common configurations (Fig. 1.6).

Horizontal-Axis Wind Turbines (HAWT) The horizontal-axis wind turbine (HAWT) is widely adopted and typically installed on towers, as depicted in Fig. 1.7. The HAWT offers notable advantages, including high efficiency and a favorable cost-to-power ratio. However, it does come with

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1 Wind Turbine Applications Overview

Fig. 1.6 Types of wind turbine axis Fig. 1.7 Horizontal wind turbine description

certain drawbacks, such as a complex design and challenges associated with maintenance. Due to the need for mounting the generator and gearbox on the tower, maintenance and servicing can be more demanding compared to other configurations.

Vertical-Axis Wind Turbine (VAWT) The initial windmills constructed utilized a vertical-axis structure, with two prominent designs being the Savonius rotor (Fig. 1.8) and the Darrius rotor (Fig. 1.9). Vertical-axis wind turbines (VAWT) offer certain advantages, such as simplified maintenance and the ability to capture wind from any direction. Additionally, their blade design is straightforward, resulting in lower fabrication costs. However, VAWTs do have a few drawbacks. They require a generator to operate in motor mode

1.3 Global Structure of a Conversion Wind System

13

Fig. 1.8 Vertical Savonius wind turbine description

Fig. 1.9 Vertical Darius wind turbine description

Vwind

during startup, and the presence of a high aerodynamic torque component leads to lower efficiency and increased oscillations. We can resume the wind turbines classification which depends on orientation of the shaft and rotational axis in Table 1.3. Efficiency and description of wind turbine axis are given in Table 1.4.

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1 Wind Turbine Applications Overview

Table 1.3 Classification of wind turbines based on shaft orientation and rotational axis Horizontal-Axis Wind Turbines (HAWT) – Mounted on towers – High efficiency – Low cost/power ratio – Complex design – Maintenance challenges due to tower-mounted generator and gearbox Vertical-Axis Wind Turbines (VAWT) – Simple blade design – Low fabrication cost – Easy maintenance – Requires generator to run in motor mode at start – Can receive wind from any direction Table 1.4 Efficiency and description of wind turbine axis

Wind turbines

Efficiency [1] (%) Description

HAWT One blade

13

Two blades

47

Three blades 50

VAWT

Darius

40

Savonius

16

1.3 Global Structure of a Conversion Wind System

1.3.2.2

15

Mechanical Gearbox

The mechanical linkage between an electrical generator and the rotor of a wind turbine can be established either directly or through the use of a gearbox. Specifically, the gearbox (G) serves the purpose of transforming the rotational speed generated by the turbine blades into a suitable rotational speed for the electrical generator (EG). This conversion enables the electrical generator to efficiently convert the mechanical energy derived from the wind into electrical energy [2–4]. ωt =

ωg G

(1.1)

The tip-speed ratio (TSR) for wind turbines is defined as the ratio between the rotational speed of the blade tip and the actual wind speed (Table 1.5). It represents the relative speed at which the blade tip moves through the air compared to the speed of the wind itself [5, 6]. λ=

ωt .R Vwind

(1.2)

Modern horizontal-axis wind turbine use generally λ of nine to ten for two bladed rotors and six to nine for three blades [1].

Power Coefficient The power coefficient of a wind turbine is a metric that quantifies the efficiency with which the turbine converts the energy present in the wind into electrical power. It serves as a measure of the turbine’s ability to harness the available wind energy. The power coefficient is influenced by the axis of the wind turbine, as depicted in Fig. 1.10. Different wind turbine designs and orientations can result in variations in the power coefficient. Here: • The Betz Limit: Also known as Betz’s law or Betz coefficient, it defines the maximum theoretical efficiency of a wind turbine rotor in converting kinetic energy from the wind into mechanical energy. According to Betz’s law, no wind turbine can capture more than 16/27 (or approximately 59.3%) of the kinetic energy in the wind. This limit is derived from considerations of conservation of mass and momentum in the airflow through the rotor. Table 1.5 Tip-speed ratio number

λ

Value

1–2

Low

10

High

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1 Wind Turbine Applications Overview

Fig. 1.10 Power coefficient for different wind turbine axes

• Ideal Propeller: It refers to a theoretical concept in aerodynamics where a propeller operates with perfect efficiency. In an ideal scenario, the propeller would convert all the power from the engine into thrust, and there would be no losses due to factors such as friction, turbulence, or other inefficiencies. While ideal propellers are useful for theoretical analysis, real-world propellers always exhibit some level of inefficiency. • American Multiblades: The term “American multiblades” is not a widely recognized term in the context of wind turbines or propellers. It could potentially refer to a specific design or type of multiblade wind turbine developed or popularized in the United States. Without additional context or specifications, it’s challenging to provide a more precise definition. • Savonius Rotor: It is a type of vertical-axis wind turbine (VAWT) characterized by its simple design and ability to capture wind from any direction. It typically consists of two or more scoop-shaped blades arranged in a vertical orientation. The Savonius rotor is known for its reliability and ability to operate at low wind speeds. However, its efficiency is generally lower compared to some other wind turbine designs. • Darrieus Rotor: It is another type of vertical-axis wind turbine (VAWT) with a distinctive design featuring curved blades that resemble the shape of an eggbeater. The rotor is often mounted on a vertical shaft. Darrieus turbines are known for their ability to start rotation at lower wind speeds compared to some other designs. However, they may face challenges at high wind speeds, and various modifications have been made to enhance their performance.

1.3 Global Structure of a Conversion Wind System

17

V

V1

V2

Fig. 1.11 Wind velocity direction throws the rotor plane of wind turbine

Power Coefficient Expressions We can express power coefficient with different expressions [7, 8]. Power Coefficient in Terms of Axial Induction Factor (a) The axial induction factor (a) is a parameter that represents the reduction in the axial component of velocity in a wind turbine. It quantifies the fraction by which the velocity decreases. If the free stream velocity is denoted as V 1 and the axial velocity at the rotor plane is denoted as V 2 , the axial induction factor can be calculated as shown in Fig. 1.11. a=

V1 − V2 V1

(1.3)

The power coefficient C p can be expressed in terms of axial induction factor (a) [7]: C p = 4a · (1 − a)2

(1.4)

Power Coefficient in Terms of the Tip-Speed Ratio (TSR) The power coefficient C p can be expressed in terms of the tip-speed ratio λ as [1, 7]: C p (λ) = −0.2121 · λ3 + 0.0856 · λ2 + 0.2539 · λ

(1.5)

18

1 Wind Turbine Applications Overview

Power coefficient

0.2

0.15

0.1

0.05

0 0

0.2

0.4

0.6

0.8

1.1

1.3

Tip speed ratio Fig. 1.12 Power coefficient in terms of TSR

The mentioned expression for the axial induction factor is commonly employed for Savonius turbines or small American multiblade turbines with power capacities below 0.1 kW. Figure 1.12 provides a graphical representation of this relationship. The Power Coefficient in Terms of TSR λ and Pitch Angle Beta β The incidence angle refers to the angle formed between the relative velocity of the wind (which is a vector velocity resulting from the wind’s axial rotor velocity and its rotational velocity) and the plane of rotation. We defined the pitch angle and the angle of incidence in Fig. 1.13.     R.ωtb 1 = arctg i = arctg λ vwind

(1.6)

Here Vres is a vector velocity resulted from the wind axial rotor velocity and Vrot is the wind rotational velocity.

1.3 Global Structure of a Conversion Wind System

19



V rot

 V

Movement direction

i

Vres

Fig. 1.13 Pitch control

Table 1.6 Values of the coefficients c1 , c2 , c3 , c4 , c5 , and c6

Coefficient

Value

c1

0.5176

c2

116

c3

0.4

c4

5

c5

21

c6

0.0068

In this case, the power coefficient can be expressed in terms of constant coefficients [8]:  C p (λ) = c1

 c5 c2 − c3 · β − c4 · e λi + c6 · λ λi

(1.7)

where c1 , c2 , c3 , c4 , c5 , and c6 are constant coefficients (Table 1.6). The expression (1.7) can then be written as  C p(λ) = 0.5176 ·

 116 − 21 − 0.4.β − 5 · e λi + 0.0068 · λ λi

(1.8)

20

1 Wind Turbine Applications Overview

0.50 0°

Power coefficient

0.40

5° 10

0.30

15 20

0.20 0.10 0.00 0.00

4.00

8.00 12.00 Tip speed ratio

16.00

20.00

Fig. 1.14 Power coefficient for different pitch angle beta using expression (1.8)

where 0.035 1 1 − 3 = λi λ + 0.008 · β β +1

(1.9)

We can represent the power coefficient for different pitch angle beta in Fig. 1.14. • We can have the simplified expression (1.8)  C p(λ) = 0.5176 ·

 116 − 21 − 0.4 · β − 5 · e λi λi

(1.10)

In this case, the power coefficient can be represented as Fig. 1.15). • The power coefficient C p is related to tip-speed ratio λ, and rotor blade pitch angle  C p (λ, β) = (0.44 − 0.0167β) sin

π (λ − 3) 15 − 0.3β

 − 0.00184(λ − 3) β (1.11)

We can represent the power coefficient for different pitch angles beta in Fig. 1.16. It can be observed that for a fixed pitch angle (β), the wind turbine achieves a maximum power coefficient (C p ) when the tip-speed ratio (λ) reaches its optimal value.

1.3 Global Structure of a Conversion Wind System

21

0.50

Power coefficient

0° 5°

0.40

10° 0.30

15° 20°

0.20 0.10 0.00 0

2

3

4

5

6

7

8

9

10 11 12 14 16 18 20

Tip speed ratio Fig. 1.15 Power coefficient for different pitch angle beta using expression (1.10)

0.50 0° Power coefficient

0.40

5° 10°

0.30

15° 20°

0.20 0.10 0.00 0

2

4

6

8

10

12

14

16

18

20

Tip speed ratio Fig. 1.16 Power coefficient as a function of tip-speed ratio and pitch angle

• Cp in an approximate relationship The following closed-from approximate relationship for C p used: 1 C p (λ, β) = · 2



 R 0.17.R 2 − 0.022 · β − 5.6 · e− λ . λ

(1.12)

22

1 Wind Turbine Applications Overview

0.6

Cp(Vwind)

0.5 0.4 0.3 0.2 0.1 0 Vwind(m/s) Fig. 1.17 Power coefficient obtained with an approximation function using polynomial interpolation

Power Coefficient in Terms of Wind Speeds We express the power coefficient by an approximation function using polynomial interpolation [7]   2 3 C p Vwind) = 1.1072 − 1.2698 ∗ Vwind − 0.493 ∗ Vwind − 0.0008 ∗ Vwind 4 5 + 0.0781 ∗ Vwind − 4.27 ∗ exp( − 4) ∗ Vwind + 1.37 ∗ exp( − 5) 6 7 8 ∗ Vwind − 2.44 ∗ exp( − 7) ∗ Vwind + 1.83 ∗ exp( − 9) ∗ Vwind (1.13)

It can be represented as shown in Fig. 1.17.

1.3.2.3

Wind Power Expression

The aerodynamic power of the wind through a wind disk of radius R is given by [9–11]: Pwind =

1 3 · ρ · π · R 2 · Vwind 2

(1.14)

where ρ represents the air density (ρ = 1.235 kg/m3 ) and Awind = π · R 2 is the swept area. For example for a small turbine (R = 1 m), we obtain the following curve (Fig. 1.18) for different wind speeds.

1.3 Global Structure of a Conversion Wind System

23

18000

Wind power (W)

16000 14000 12000 10000 8000 6000 4000 2000 0 3.5

5

6

7

8

9

10

12

15

Wind speed (m/s) Fig. 1.18 Aerodynamic power versus wind speeds

We can also express the wind power by an approximation function using polynomial interpolation [7]: 2 3 4 Pwind = 4240 − 4727 ∗ Vwind − 2194 ∗ Vwind − 562 ∗ Vwind + 88.5 ∗ Vwind 5 6 7 − 8.91 ∗ Vwind + 0.585 ∗ Vwind − 0.0249 ∗ Vwind 8 + 6.64 ∗ exp( − 4) ∗ Vwind

(1.15)

It can be represented in Fig. 1.19. 3,50,000.00

Wind power (kW)

3,00,000.00 2,50,000.00 2,00,000.00 1,50,000.00 1,00,000.00 50,000.00 0.00 0

3.5

5

7

8

9

Wind speed (m/s) Fig. 1.19 Aerodynamic power obtaining with polynomial interpolation

10

12

15

24

1 Wind Turbine Applications Overview

0.08

0° 5°

Torque coefficient

0.06

10 15 20

0.04

0.02

0.00 0.00

4.00

8.00 12.00 Tip speed ratio

16.00

20.00

Fig. 1.20 Torque coefficient

The aerodynamic torque expression is deduced by Pwind = ωtb · Twind

(1.16)

Thus: T ewind =

1 2 · C T (λ, β) · ρ · π · R 3 · Vwind 2

(1.17)

where C T (λ, β) is the torque coefficient (TC), it depends on the power coefficient C P (λ, β) and it is defined as C T (λ, β) =

C P (λ, β) λ

(1.18)

The torque coefficient can be represented as shown in Fig. 1.20. The mechanical power, which is converted by a wind turbine, Pt is dependent on the power coefficient C p (λ, β). It is given by PT b =

1 3 · C p (λ, β) · ρ · π · R 2 · Vwind 2

(1.19)

A wind turbine can only convert just a certain percentage of the captured wind power. This percentage is represented by C p (λ, β) which is a function of the wind speed, turbine speed, and the blade pitch angle β (Fig. 1.21).

1.3 Global Structure of a Conversion Wind System

25

Fig. 1.21 Power conversion in a wind turbine

The C P (λ, β) curve has a unique maximum [C P (λ, β)]opt that corresponds to a maximum power. Here λopt =

ωt−opt vwind

(1.20)

The theoretical maximum power coefficient value is given by the Betz limit C P,max =

16 ≈ 0.5926 27

(1.21)

The theoretical maximum wind power will be Pmax =

1 16 3 · · ρ · π · R 2 · Vwind 2 27

(1.22)

Thus: 3 Pmax = 0.3644 ∗ π · R 2 · Vwind

(1.23)

In this case, the maximum power for different speeds can be represented in Fig. 1.22.

26

1 Wind Turbine Applications Overview

1,000.00 Maximum Power (kWatt)

900.00 800.00 700.00 600.00

R=1m

500.00

R=1.5m

400.00

R=2m

300.00

R=3m

200.00

R=5m

100.00 0.00 Wind speeds (m/s) Fig. 1.22 Maximum power for different wind speeds

We can also obtain the maximum power coefficient by the following expression [7]:

C p,max

24 = 2 · λ

a2  a1

(1 − a) · (1 − 2a) · (1 − 4a) (1 − 3a)

2 · da

(1.24)

where a1 corresponds to the axial induction factor for λ = 0 and a2 corresponds to the axial induction factor for λ equal to the local speed ratio. It can be obtained in the following curve (Fig. 1.23).

1.3.3 Load In a wind power system, the load refers to the electrical devices or consumers that utilize the generated power. The load can be classified into two main categories: autonomous load and grid utility. Table 1.7 gives an overview of the load types in a wind power system, including their characteristics, advantages, and drawbacks.

1.3.3.1

Autonomous Load

An autonomous load in a wind power system refers to the local electrical load that is directly powered by the generated wind energy without being connected to the utility

1.3 Global Structure of a Conversion Wind System

27

0.6 0.5

Cpmax

0.4 0.3 0.2 0.1 0 0

0.5

1

1.5

2

2.5

5

7.5

10

lamda Fig. 1.23 Ideal theoretical maximum power coefficient Table 1.7 Load types in a wind power system Load type

Characteristics

Advantages

Drawbacks

– Enables electrification in remote areas

– Limited scalability

– Standalone power system without grid connection

– Provides energy independence and self-sufficiency

– Higher initial costs and maintenance requirements for energy storage

– Relies on wind turbine and energy storage systems

– Reduces reliance on fossil fuels

– Challenges in matching intermittent wind power with varying load demand

Autonomous – Local load load directly powered by generated wind energy

Grid utility

– Load connected – Efficient utilization to utility grid of wind power by integrating with existing grid infrastructure

– Grid dependency and vulnerability to grid disruptions

– Wind turbine supplies power to the load

– Flexibility in meeting – Compliance with grid load demand interconnection standards and variations regulations

– Excess power can be exported to the grid

– Possibility of net – Potential challenges related to metering or selling power quality, voltage excess power back to regulation, and grid stability, the grid especially in high-penetration systems

28

1 Wind Turbine Applications Overview

grid. Even all attention is on powerful machines, it is observed an increasing demand for smaller units to be installed near homes or buildings to use directly electrical energy. Induction generator is widely used for the production of electricity from wind energy, especially in remote and isolated areas. With all its advantages (simplicity, robustness, low maintenance, small size per kW generated), it’s the generator the most used in the production of low power in isolated or autonomous operation. The “small wind” covers the power range from 20 W to 100 kW with three categories [12]: • micro wind turbines from 20 to 500 W, • mini wind turbines from 500 W to 1 kW, • small wind turbines with 1–100 kW. In the case of micro and mini wind turbines, the power output is 0, which results in an increase of the delivered mechanical power and change of the operating point Xi (i = 1, 2,..., n−1). In this case, the rotational speed and the Pm power increase up to a new point

5.2 Maximum Power Point Tracking (MPPT) Algorithms

155

Fig. 5.2 Flowchart of the P&O method

Xi + 1. Similar steps with opposite direction can be done in the case of a decrease of the mechanical power, by setting X = ωm , the instantaneous rotational speed of the wind turbine follows the maximum power point according to a predetermined rotational speed and power values. Under these conditions, the tracker seeks the MPP permanently. At specified wind speed, the desired mechanical power is the solution of the nonlinear equation given by d Pm /dωm = 0

(5.1)

The controlling rule for adjusting the step-size varies from one group of studies to another, depending on the disturbed variable [9, 15], and [30]. The magnitude of the step-size is the main factor determining the amplitude of oscillations that allows the convergence rate to the final response. Nevertheless a larger disturbance will lead to a higher value of oscillation amplitude around the peak point. In this algorithm there is a trade-off between the rate of response and the amount of oscillations under steady state conditions. To overcome this trade-off, the step-size of varying amplitude can be applied. The step-size amplitude can be determined according to power variations based on the previously applied disturbance. Therefore, larger step-size amplitude

156

5 Optimization Methods Used in WECSs

MPP Turbine power (w)

Xn-1

Xn Xn+1

X2 X1

Generator speed (rd/s)

Fig. 5.3 The principle of the P&O MPPT

is selected when power is far from MPP due to the larger magnitude of Pm (ωm ) slope and small amplitude is selected when power is close to MPP. The step-size is continually decreased until it approaches zero in the aim to drive the operating point to settle down at the MPP. The speed step and the reference generator speed is computed as: ⎧ ⎨ ω (k) = C. d Pm (k) m dωm (k) ⎩ ∗ ωm (k + 1) = ωm (k) + ωm (k)

(5.2)

where: ωm (K) and ωm (K + 1) are generator speed values at sampling time (k) and (k + 1) and (C) is the step change. Application under Matlab/Simulink: We make an application of the P&O method in a wind system. The block diagram under Matlab/Simulink can be represented in Fig. 5.4. The turbine model is obtained using equations given in Chap. 1. We can implement it under Matlab/Simulink in Fig. 5.6. The P&O method is represented in Fig. 5.7 (Fig. 5.5). We make an application with a chosen wind speed profile (Fig. 5.8). We note that the power coefficient Cp kept constant (Fig. 5.9) whatever the wind variations and the reference torque follows the turbine torque (Figs. 5.10 and 5.11). We note also that the turbine speed follows the wind profile (Fig. 5.12).

5.2 Maximum Power Point Tracking (MPPT) Algorithms

157

ωm

Pm

Turbine

Popt

HCS(P&O) algorithm

Rectifier ωm

ωm-opt +

load

EG

Control

ωm

Tem-ref

Fig. 5.4 Hill Climb search control of wind energy conversion system

Wm

Wm Vw

Product

Scope4

Tt

Clock2 Look-Up Table

Tem_ref

Scope1 Wm_ref

Cp

lambda Pt

model turbine

W opt

Cp

Wm

Scope2

P&O method Lamda

Fig. 5.5 Block diagram of WTCS with P&O method

5.2.2 Tip Speed Ratio Method (TSR) To achieve maximum power output, it is desirable for the wind turbine to consistently operate at its optimal tip speed ratio (λopt). The Tip Speed Ratio (TSR) control method is employed to regulate the tip speed ratio at an optimal value, where the rotational speed of the turbine is at its optimum and the power extraction is maximized. This control strategy necessitates knowledge of the wind speed, turbine speed, and the

158

5 Optimization Methods Used in WECSs

G1 1 5 lambda Product

R

Cp(lambda)1 R1

-K-

1

4 Cp

1

Vw

f(u)

1

1

1

den(s)

Wm

G

Step

2 Tt Epi

2

Spi

Wm_ref

3 Tem_ref

PI regulator

Scope2 Scope1

-CConstant cpopt (u(1)*u(2))/u(3) Constant1

Fig. 5.6 Turbine model under matlab/Simulink

Dp 1

>

P

0 Memory

2 Wt

C

0


0 if S(x) < 0 ⎩• S (x) < 0 if S(x) > 0

(5.11)

It can be written as: •

S (x) · S(x) < 0

(5.12)

• Lyapunov function The Lyapunov function is a positive scalar function for the state variables of the system. The idea is to choose a scalar function to ensure the attraction of the variable to be controlled to its reference value.

5.5 Sliding Mode Control

171

We define the Lyapunov function as follows: V (x) =

1 2 · S (x) 2

(5.13)

The derivative of this function is: •

•

V (x) = S(x) · S (x)

(5.14)

The function will decrease, if its derivative is negative. This is checked only if the condition (Eq. 5.12) is verified. • Determination of the control law The control law is responsible for driving the system towards the sliding surface and maintaining it there. It is designed to generate control signals that achieve the desired system behavior and compensate for uncertainties and disturbances. The structure of a sliding mode  controller consists of two parts. The first one concerns the exact linearization u eq and the second one concerns the stabilization (u n ). u = u eq + u n

(5.15)

where: u eq corresponds to the control. It serves to maintain the variable control on the sliding surface. u n is the discrete control determined to check the convergence condition (Eq. 5.12). We consider a system defined in state space, and we have to find analogical expression of the control (u). •

S (x) =

∂S ∂x ∂S = · ∂t ∂ x ∂t

(5.16)

Substituting Eq. 5.10 and Eq. 5.14 in Eq. 5.16, we obtain. •

S (x) =

∂S ∂S  · A(x, t) + B(x, t) · u eq + · B(x, t) · u n ∂x ∂x

(5.17)

We deduce the expression of the equivalent control. u eq

−1  ∂S ∂S · B(x, t) · A(x, t) =− · ∂t ∂t

(5.18)

For the equivalent control can take a finite value, it must. ∂S · B(x, t) = 0 ∂x

(5.19)

172

5 Optimization Methods Used in WECSs

In the convergence mode and replacing the equivalent control by its expression, we find the new expression of the surface derivative: •

S (x, t) =

∂S · B(x, t) · u n ∂x

(5.20)

And the condition expressed by Eq. 112 becomes S(x, t) ·

∂S · B(x, t) · u n < 0 ∂x

(5.21)

The simplest form that can take the discrete control is as follows: u n = ks · sign(S(x, t)) where the sign of k s must be different from that of

∂S ∂x

(5.22) · B(x, t).

5.6 Fuzzy Logic Controller Technique A fuzzy logic controller is introduced to determine the operating point corresponding to the maximum power for different wind speeds. The controller takes power variation (Pwind) and speed variation (t) as inputs and generates a reference voltage variation (t,ref) as an output. The goal is to converge towards the optimal operating point. Establishing the rules for the fuzzy logic controller is relatively simple. These rules depend on the variations of power (Pwind) and voltage (t). Referring to Table 5.3, if the power (Pwind) increases, the operating point should also be increased. Conversely, if the power (Pwind) decreases, the speed (t,ref) should be adjusted accordingly. By employing fuzzy logic, the controller can handle the imprecise and uncertain nature of the system. The rules are designed to capture the relationships between the input variables and output to ensure optimal performance. Through fuzzy inference and membership functions, the controller can make decisions and adjust the operating point based on the current variations in power and speed. From these linguistic rules, the MPPT algorithm contains measurement of variation of wind power Pwind and variation of turbine speed t proposes a variation of t,ref according to Eq. 117. ⎧ ⎨ Pwind = Pwind (k) − Pwind (k − 1) t = t (k) − t (k − 1) ⎩ t−r e f (k) = t (k − 1) + t−r e f (k)

(5.23)

where Pwind (k) and t (k) are the power and speed of the turbine at sampled times (k), and t,ref (k) the instant of reference speed.

5.6 Fuzzy Logic Controller Technique

173

Table 5.3 Fuzzy rules table Pwind

BN

MN

SN

Z

SP

MP

BP

t BN

BP

BP

MP

Z

MN

BN

BN

MN

BP

MP

SP

Z

SN

MN

BN

SN

MP

SP

SP

Z

SN

SN

MN

Z

BN

MN

SN

Z

SP

MP

BP

SP

MN

SN

SN

Z

SP

SP

MP

MP

BN

MN

SN

Z

SP

MP

BP

BP

BN

BN

MN

Z

MP

BP

BP

The block fuzzy logic controller includes three functional blocks: fuzzification, fuzzy rule algorithm, and defuzzification (Fig. 5.30). Figure 5.31 shows the membership function of input and output variables in which membership functions of input variables Pwind , t is triangular and has seven fuzzy subsets. Seven fuzzy subsets are considered for membership functions of the output variable t,ref… These inputs and output variables are expressed in terms of linguistic variables (such as BN (big negative), MN (means negative), SN (small negative), Z (zero), SP (small positive), MP (means positive), and BP (big positive). We can represent the WECS controlled by FLC as in Fig. 5.32.

Pwind[k]

Pwind

+ -

Pwind[k-1]

Ωt

Ωt [k]

FLC MPPT

ΔΩt ,ref[k] +

Ωt ,ref[k]

+

+

Speed -

+

regulator

-

Ωt [k-1]

Ωt,m

Fig. 5.30 Structure of fuzzy controller MPPT wind applied to the system

Tem_ref

174

5 Optimization Methods Used in WECSs

μ(ΔPwind) BN

MN

Z

SN 1

-1

-2/3

-1/3

0 0

SP

1/3

BP

MP

1

2/3

ΔPwind, ΔΩwind

μ( ΔPwind,ref) BN MN

-1

SN

Z

0

-2/3 -1/3

SP

MP

BP

1/3

2/3

1

ΔΩt

Fig. 5.31 Membership functions of Pwind, t and Pwind,ref

5.7 Adaptative Fuzzy Logic Controller (AFLC) The AFLC is improved from scaling Fuzzy logic controller (FLC) If any one of its tunable parameters (membership functions, fuzzy rules and scaling factors) changes when the controller is being used, if not it is a conventional Fuzzy controller. The error (e) and the variation error (e) of the system and of the modifier based learning are used to modify the fuzzy parameters to optimize system operation. The errors are given by [35]. e(k) =

Pwind (k + 1) − Pwind (k) t (k + 1) − t (k)

(5.24)

And the error variation e(k) is e(k) = e(k + 1) − e(k)

(5.25)

The input e(k) shows if the load operation point at the instant k is located on the left or on the right of the maximum power point, while the input e(k) expresses the moving direction of this point. The fuzzy parameters can be adjusted using the following condition: If e < ε (limit value), then the modifier based learning will be

5.7 Adaptative Fuzzy Logic Controller (AFLC)

EG

175

AC/DC

DC/AC

3------2 Fuzzy controller Control

ωt

Fuzzy controller Control

Pg Vdcref

Vgref

Fig. 5.32 Wind energy conversion system controlled by fuzzy logic controller

selected. The controller MAMDANI type with seven classes’membership functions is represented in Table 5.4. The AFLC method is composed of two parts: The fuzzy logic control and adaptive mechanism. The FLC is one part of AFLC, which is composed of three units: fuzzification, fuzzy rules and defuzzification [35] (Fig. 5.33). Figure 5.34. shows the membership function of AFLC. Table 5.4 Modified Fuzzy rules table Error(e)

Variation error (ε) NB

NM

NS

Z

PS

PM

PM

NB

NB

NB

NM

Z

Z

Z

Z

NM

NB

NM

NM

Z

NM

PS

PS

NS

NB

NB

NB

NB

PM

PS

PM

Z

NB

NB

NS

Z

PS

PM

PB

PS

NM

NS

Z

PS

PM

PB

PB

PM

NS

PB

PB

PB

PB

PB

PB

PB

Z

PB

PB

PB

PB

PB

PB

176

5 Optimization Methods Used in WECSs Fuzzy Knowledge base controller

e(k) Δe(k)

FUZZIFICATION

INFERENCE

DEFFUZIFICATION

Rules

Knowledge base • • •

Inverse Fuzzy model

Knowledge-base Modifier Scalling Fuzzy set Rules base

Learning mechanism

Fig. 5.33 Block diagram of adaptative Fuzzy logic controller

Fig. 5.34 Membership functions of AFLC method

5.8 Artificial Neural Networks (ANN) Method Artificial neural networks (ANN) are electronic models based on the neural structure of the brain. This function permits ANNs to be used in the design of adaptive and intelligent systems since they are able to solve problems from previous examples. ANN models involve the creation of massively paralleled networks composed mostly of nonlinear elements known as neurons. Each model involves the training of the paralleled networks to solve specific problem [34]. ANNs consist of neurons in layers, where the activations of the input layer are set by an external parameter. Generally, networks contain three layers—input, hidden, and output. The input layer receives data usually from an external source while the output layer sends information

5.9 Radial Basis Function Network (RBFN)

177

to an external device. There may be one or more hidden layers between the input and output layers. The back-propagation method is the common type of learning algorithm [34].

5.9 Radial Basis Function Network (RBFN) Radial Basis Function Network has a similar feature to fuzzy system. The output value is calculated using the weighted sum method and the number of nodes in the hidden layer of the RBFN is the same as the number of if–then rules in the fuzzy system. The receptive field functions of the RBFN are similar to the membership functions of the premise part in the fuzzy system. An application of RBFN on a wind energy conversion system is represented in Fig. 5.35. The electrical generator is driven by a wind turbine supplying the power to a load (grid for example), through back to back converters. To control the DC/AC converter, we use an MPPT (P&O and RBFN) control for maximizing power and a PWM control. The reference dc voltage Vdcref is obtained using P&O method and RBFN controller force Vdc to follow its reference Vdcref and adjust the load current reference ILoadref for the PWM inverter control (Figs. 5.36, 5.37).

Turbine Rectifier ωm EG

Vwind

3 Pwind = 1 .ρ .π .R 2 .Vwind 2

C p (λ , β )

λ ωt

λ =

β ωtR Vwind

ANN

Fig. 5.35 ANN control of wind energy conversion system

load

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5 Optimization Methods Used in WECSs

Fig. 5.36 ANN training

5.10 Particle Swarm Optimization (PSO) Method PSO method applies an analogy of swarm behaviour of natural creatures (birds or fish). For example the schooling of fish and the flocking of birds. Birds usually seek food (their objective) in swarms. Each individual bird (agent) reconfigures its behavior, based on its own experience and the experiences of other [31–33].

5.10 Particle Swarm Optimization (PSO) Method

179

E DC/AC

AC/DC

Vdc

Idc

P&O

Vdcref

ILoadref

RBFN

PWM

Iload

Control MPPT

Fig. 5.37 Wind energy conversion system with RBFN controller

A swarm is a population of particles and each particle flies towards the optimum or a quasi-optimum solution based on its own experience, experience of nearby particles, and global best position among particles in the swarm (Fig. 5.38). At time t, each particle i has its position X ti and a velocity Vti in a variable space. The velocity and position of each particle change in the next generation (X ik+1 ,Vik+1 ). Where: i individual particule, X ik current position of the particule i, X ik+1 modified position of the particule i, Pbesti velocity based on personal best, Gbesti velocity based on global test,Vik current velocity of the particule i, Vik+1 modified velocity of the particule i.

X ik +1 Vi k Vi k +1

Gbest i

,

X ik

Fig. 5.38 PSO concept

X iPbest

Pbest i

180

5 Optimization Methods Used in WECSs

5.11 Adaptative Neuro-Fuzzy Inference System (ANFIS) The ANFIS controller is designed and adapted to tracking a maximum power of the wind. ANFIS is the integration of artificial neural networks and fuzzy inference systems [34]. Neural network (NN) is used to adjust input and output parameters of membership function in the fuzzy logic controller (FLC). A typical architecture of a neuro-fuzzy network for two inputs (x and y) is shown in Fig. 5.39. The first layer is called input layer. Each node of this layer stores the parameters to define a bell-shaped membership function. In the second layer, each node performs connective operation “AND” within the rule antecedent to determine the corresponding firing, the nodes of layer 3 perform a normalization process to produce the normalized firing strength. The fourth layer deals with the consequent part of the fuzzy rule. The node of this layer is adaptive with output. Finally, the fifth layer which is the final output is the weighted average of all rule outputs [34]. The first of three input signals of ANFIS is the error signal e(t), the second one is the changing of error signal depending on time de(t)/dt and the third input signal of ANFIS is the value of power Pmec .

Layer 1

A1

x

Layer 2

Layer 3

π

Ν

Layer 4

Layer 5

A2

Σ

B1 π

y

Ν

B2

Fig. 5.39 General structure of Neuro-Fuzzy controller

f

5.12 Comparison Between Different Optimization Methods

181

We have: e(t) = ωt − ωt−nom

(5.26)

de(t) ωt − ωt−nom = dt dt

(5.27)

And:

5.12 Comparison Between Different Optimization Methods Table 5.5 provides a summary of the advantages, drawbacks, and applications of the most used MPPT methods in wind energy conversion. Table 5.5 Comparison of the most used MPPT methods MPPT Method

Advantages

Drawbacks

TSR (Tip Speed Ratio)

Maximizes power extraction, simple implementation

Limited to specific wind Small to speed range, requires medium-scale wind knowledge of optimal power systems TSR

Applications

P&O (Perturb and Observe)

Simple implementation, Oscillations around widely used MPP, slow response to rapidly changing conditions

Small to medium-scale wind power systems

SMC (Sliding Mode Control)

High precision, good stability, robustness

Complex design, sensitive to model inaccuracies

Medium to large-scale wind power systems

FLC (Fuzzy Logic Control)

Handling of imprecise and uncertain systems, adaptive control

Difficulty in designing appropriate fuzzy rules, performance highly dependent on rule tuning

Small to medium-scale wind power systems

AFLC (Adaptive Fuzzy Logic Control)

Adaptive control, improved performance through online tuning

Complexity in Small to parameter adaptation, medium-scale wind potential for oscillations power systems during adaptation

PSO (Particle Swarm Optimization)

Global optimization, robustness to local optima

Computational complexity, parameter tuning

Small to medium-scale wind power systems (continued)

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5 Optimization Methods Used in WECSs

Table 5.5 (continued) MPPT Method

Advantages

Drawbacks

Applications

ANN (Artificial Neural Network)

Nonlinear mapping, adaptability to complex systems

Requires training data, potential for overfitting

Medium to large-scale wind power systems

WRBFM (Wavelet Radial Basis Function Network)

Improved modeling accuracy, efficient approximation

Computational complexity, potential for overfitting

Medium to large-scale wind power systems

RBFM (Radial Basis Function Network)

Nonlinear Potential for overfitting, Small to approximation, high sensitivity to network medium-scale wind computational efficiency structure power systems

ANFIS (Adaptive Neuro-Fuzzy Inference System)

Combination of ANN and FLC, adaptive control

Complexity in Small to parameter tuning, medium-scale wind computational overhead power systems

5.13 Conclusion There are various MPPT methods utilized in wind power systems, each with its own set of advantages, drawbacks, and applications. The choice of an MPPT method depends on factors such as system scale, wind speed range, control requirements, and accuracy needed. Classical methods like TSR and P&O offer simplicity in implementation and are commonly used in small to medium-scale wind power systems. However, they may exhibit drawbacks such as limited applicability to specific wind speed ranges and oscillations around the maximum power point. Advanced intelligent artificial methods like SMC, FLC, AFLC, PSO, ANN, WRBFM, and RBFM provide enhanced control precision, robustness, and adaptability to complex systems. These methods find applications in both small to medium-scale and medium to large-scale wind power systems, depending on their specific characteristics. Hybrid MPPT techniques combine the strengths of different classical or advanced methods to improve overall performance and address limitations. These hybrid approaches offer the potential for enhanced efficiency, stability, and robustness, making them suitable for a wide range of wind power systems. It is important to note that the selection of an MPPT method should consider the specific requirements and constraints of the wind power system. Factors such as system size, wind conditions, computational complexity, and available data play a crucial role in determining the most suitable MPPT approach. Ongoing research and development in MPPT methods for wind power systems aim to improve efficiency, adaptability, and overall performance, thereby contributing to the effective utilization of wind energy resources and the growth of renewable energy generation.

References

183

References 1. Kesraoui M, Korichi N, Belkadi A (2011) Renew Energy 36:2655–2662 2. Nakamura T, Morimoto S, Sanada M, Takeda Y (2002) Optimum control of IPMSG for wind generation system. PPC-Osaka Conference IEEE 3:1435–1440 3. Mahdi AJ, Tang WH, Jiang L, Wu QH (2010) A comparative study on variable-speed operations of a wind generation system using vector control. In: International Conference on Renewable Energies and Power Quality (ICREPQ’10), pp 1–6 4. Meharrar A, Tioursi M, Hatti M, Boudghène Stambouli A (2011) Expert systems with applications, 38, pp 7659–7664 5. Thongam JS, Ouhrouche M (2011) In Tech book chapter 15, (Fundamental and advanced topics in wind power) 6. Datta R, Ranganathan VT (2003) A method of tracking the peak power points for a variable speed wind energy conversion system. IEEE Trans Energy Convers, 18(1) 7. Wang Q, Chang L (2004) An intelligent maximum power extraction algorithm for inverterbased variable speed wind turbine systems. IEEE Trans Power Electron 19(5):1242–1249 8. Gwabavu M, Bansal R, Naidoo R, Pemba S (2023) Onshore wind farm optimization and control, 2023 IEEE Africon, Nairobi, Kenya, pp 1-6. https://doi.org/10.1109/AFRICON55910.2023. 10293310 9. Koutroulis E, Kalaitzakis K (2006) Design of a maximum power tracking system for WindEnergy-Conversion applications. IEEE Trans Industr Electron 53(2):486–494 10. Raza KSM, Goto H, Guo H, Ichinokura O (2008) A novel algorithm for fast and efficient maximum power point tracking of wind energy conversion systems. In: Proceedings of the 2008 International Conference on Electrical Machines, pp 1–6 11. Oliveira HA, Howe Wood D, de Matos JG, de Souza Ribeiro LA, Saavedra OR, Do Nascimento ACV (2023) Improved performance of wind turbines using a hybrid MPPT strategy. In: 2023 25th European Conference on Power Electronics and Applications (EPE’23 ECCE Europe), Aalborg, Denmark, 2023, pp. 1–9, https://doi.org/10.23919/EPE23ECCEEurope58414.2023. 10264451 12. Soetedjo A, Lomi A, Mulayanto WP (2011) Modeling of wind energy system with MPPT control. In: International Conference on Electrical Engineering and Informatics, Bandung, Indonesia, 17–19 13. Whei-Min Lin and Chih-Ming Hong, InTech book chapter 13 (Wind Turbines, 2011) 14. Aldean AS, Al-Dhaifallah M, Saif AA, Elferik S, Tracking MPP, (MPPT) enhancement of Variable-Speed wind energy conversion using sliding mode controller (SMC), (2023) IEEE PES GTD International Conference and Exposition (GTD). Istanbul, Turkiye 2023:144–150. https://doi.org/10.1109/GTD49768.2023.00054 15. Chojaa H et al (2023) Robust control of DFIG-Based WECS integrating an energy storage system with intelligent MPPT under a real wind profile. IEEE Access 11:90065–90083. https:// doi.org/10.1109/ACCESS.2023.3306722 16. Raza KSM, Goto H, Guo H, Ichinokura O (2010) Review and critical analysis of the research papers published till date on maximum power point tracking in wind energy conversion system. IEEE Energy Convers Congr Expo (ECCE’2010), pp 4075–82 17. Wang Q, Chang L (2003) An intelligent maximum power extraction algorithm for InverterBased variable speed wind turbine systems. IEEE Trans Power Electron, 19(5) 18. Shirazi M, Viki AH, Babayi O (2009) A comparative study of maximum power extraction strategies in PMSG wind turbine system. In: IEEE Electrical Power and Energy Conference (EPEC’2009), pp 1–6 19. Morimoto S, Nakayama H, Sanada M, Takeda Y (2005) Sensorless output maximization control for variable-speed wind generation system using IPMSG. IEEE Trans Ind Appl, 41:1 20. Wai RJ, Lin CY, Chang YR (2007) Novel maximum-power extraction algorithm for PMSG wind generation system. IET Electr Power Appl 1(2):275–283 21. Femia N, Granozio D, Petrone G, Spagnuolo G, Vitelli M (2007) Predictive and adaptive MPPT perturb and observe method. IEEE Trans Aerosp Electron Syst, 43(3)

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22. Hui J, Bakhshai A (2008) A fast and effective control algorithm for maximum power point tracking in wind energy systems. In: the Proceedings of the 2008 World Wind Energy Conference, pp 1–10 23. Molina MG, Mercado PE (2008) A new control strategy of variable speed wind turbine generator for three-phase grid-connected applications. In: Transmission and Distribution Conference and Exposition, IEEE/PES, Bogota, Colombia, 13–15 24. Rekioua D, Matagne E (2012) Modeling of solar irradiance and cells. in: optimization of photovoltaic power systems. Green Energy Technol. Springer, London. https://doi.org/10.1007/ 978-1-4471-2403-0_2 25. Sahoo S, Thokchom S, Puhan PS, Panda G (2023) Power quality control and comparison of a micro grid system using fuzzy MPPT Technique. In: 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE), Shillong, India, pp 1–6. https://doi.org/10.1109/ICEPE57949.2023.10201624 26. Yaoqin J, Zhongqing Y, Binggang C (2002) A new maximum power point tracking control scheme for wind generation. In: International Conference on Power System Technology 2002 (PowerCon’2002). pp 144–148, 13–17 27. Baltag A, Livint G, Belehuz L, Baciu AG (2023) Application of feedback linearization method to wind turbines with PMSG for extracting maximum power from wind energy. In: 2023 10th International Conference on Modern Power Systems (MPS), Cluj-Napoca, Romania, pp 01–06. https://doi.org/10.1109/MPS58874.2023.10187595 28. Debbabi F, Mehazzem F, Soubdhan T (2023) Genetic Algorithm-based MPPT for wind power conversion system: study and comparison with conventional method in tropical climate. In: 2023 5th Global Power, Energy and Communication Conference (GPECOM), Nevsehir, Turkiye, pp 218-224. https://doi.org/10.1109/GPECOM58364.2023.10175822 29. Dulal Ch Das, Roy AK, Sinha N (2011) PSO optimized frequency controller for Wind-Solar thermal-Diesel hybrid energy generation system: a study. Int J Wisdom Based Comput, 1(3), pp128-133 30. Mohandes M et al (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy. https://doi.org/10.1016/j.apenergy.2011.04.015 31. Dadone A, Dambrosio L (1999) Estimator-Based adaptive fuzzy logic control technique for a wind Turbine-Induction generator system. In: Proceedings of the 7th Mediterranean Conference on Control and Automation (MED99) Haifa, Israel - 28–30 32. Swierczynski M, Teodorescu R, Rasmussen CN, Rodriguez P, Vikelgaard H (2010) Overview of the energy storage systems for wind power integration enhancement. In: Proceedings of the IEEE International Symposium on Industrial Electronics, ISIE 2010. IEEE Press. p 3749–3756 33. Wei Wang (2012) Vanadium Redox flow batteries Improving the performance and reducing the cost of vanadium redox flow batteries for large-scale energy storage, report of Pacific Northwest National Laboratory 34. Comparison of different battery technologies, (2006–5–26) General electronics battery co., Ltd. p.1–4; www.tradekorea.com/product/file/download.mvc;...TK 35. Doris L, Britton Thomas B Miller (2000) Battery fundamentals and Operations-Batteries for dummies

Chapter 6

Modeling of Storage Energy Systems Used in WECS

6.1 Introduction Energy storage plays a crucial role in wind energy systems and hybrid wind systems by mitigating power fluctuations, increasing system flexibility, and facilitating the storage and dispatch of electricity generated by variable renewable energy sources like wind and solar. Various storage technologies are employed to complement these systems, including electrical, chemical, or electrochemical, mechanical, and thermal storage. By incorporating energy storage into wind energy systems or hybrid wind systems, the overall reliability and performance of the systems can be improved. Energy storage allows for better integration of renewable energy sources into the grid, enhances system stability, and enables the utilization of surplus energy during periods of low demand. It also provides the ability to store energy during favorable conditions and dispatch it during periods of high demand or when wind resources are limited. Continued advancements and research in energy storage technologies are essential for further optimizing wind energy systems and enabling a more sustainable and resilient energy infrastructure. There are various energy storage technologies employed in wind turbine power systems. It is summarized in Fig. 6.1.

6.2 Electrochemical Storage 6.2.1 Electrochemical Batteries The desired battery is obtained when two or more cells are connected in an appropriate series/parallel arrangement to obtain the required operating voltage and capacity for a certain load [1] (Fig. 6.2).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 D. Rekioua, Wind Power Electric Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-031-52883-5_6

185

186

6 Modeling of Storage Energy Systems Used in WECS

Fig. 6.1 Energy storage technologies used in wind turbine power systems

Fig. 6.2 Battery cell composition

In the market, there are many different types of batteries and most of them are subject to further research and development. In PV systems, several types of batteries can be used: nickel-Cadmium (NiCd), Nickel-Zinc (Ni-Zn), lead-acid. Nevertheless, it must have some important properties such as high charge or discharge efficiency, low self-discharge, and long life under cyclic by charging or by discharging.

6.2.1.1

Nickel-Cadmium (NiCd) Batteries

The NiCd batteries are commonly known as relatively cheap and robust. The positive nickel electrode is a nickel hydroxide/nickel oxyhydroxide (Ni(OH)2 /NiOOH)

6.2 Electrochemical Storage

187

compound, while the negative cadmium electrode consists of metallic cadmium (Cd) and cadmium hydroxide (Cd(OH)2 ). The electrolyte is an aqueous solution of potassium hydroxide (KOH). Due to its disadvantage of life span and environmental technology of cadmium, NiCd is not very applicable in WECS.

6.2.1.2

Nickel-Hydrogen Batteries

Nickel-hydrogen battery has some advantages as long cycle, resistance to overcharge, and good energy density, but it has high cost, high cell pressure, and low volumetric energy density. It is used generally in space applications and communication satellites.

6.2.1.3

Nickel-Metal Hydride Batteries

These batteries are used generally commercial consumer product. Their disadvantages are high self-discharge and failure leading to high pressure.

6.2.1.4

Nickel-Zinc Batteries

The positive electrode is nickel oxide but the electrode negative is composed of zinc metal. In addition to better environmental quality, this type of battery has a high energy density (25% higher than nickel-cadmium).

6.2.1.5

Lead-Acid Batteries

The lead-acid batteries are the most used in PV applications especially in stand-alone power systems because they are safely used and ease to transport. The lead-acid battery consists of two electrodes immersed in a sulfuric acid electrolyte (Fig. 6.3). The negative one is attached to a grid with sponge metallic lead, and the positive one is attached to a porous grid with granules of metallic lead dioxide. There are two types of lead acid batteries: • flooded (FLA) • valve-regulated (VRLA). In wind energy conversion system, electrochemical batteries are often used due to their advantages, but, due to their disadvantages (smaller power density, low depth of discharge, life cycle, lifetime, etc.), researches are focused on ultra-battery (see Sect. 4.5.1) which is battery with integrated supercapacitor in one unit cell providing high power discharge and charge with a long, low-cost life [2].

188

6 Modeling of Storage Energy Systems Used in WECS

Fig. 6.3 Lead-acid battery

6.2.1.6

Sodium-Sulfur (NaS) Batteries

In a sodium-sulfur battery, sodium and sulfur are in liquid form and are the electrodes, sodium being the cathode and sulfur being the anode. They are separated by alumina which has the role of electrolyte (Fig. 6.4). This one allows only the positive sodium ions to go through it and combine with the sulfur to form sodium polysulfide. This type of battery has a high energy density, high efficiency of charge/discharge (89– 92%), and long cycle life, and it is fabricated from inexpensive materials. Due to their advantages (high energy density, high number of cycles), NaS batteries are very used in WECS. Fig. 6.4 Sodium-Sulfur cell

6.2 Electrochemical Storage

6.2.1.7

189

Sodium-Nickel Chloride Batteries

Sodium-nickel chloride battery is also known as ZEBRA (Zero Emission Battery Research Activity) battery and it’s a system operating at around 270–350 °C. The chemical reaction in the battery converts sodium chloride and nickel to nickel chloride and sodium during the charging phase. During discharge, the reaction is reversed. Each cell is enclosed in a robust steel case. A ZEBRA battery is designed for a 2-hour discharge with peak power capability as required. They are used in WECS because of their advantages as high energy density and resistance for short circuits.

6.2.1.8

Lithium-ion(Li-ion) Batteries

The operation of Li-ion batteries is based on the transfer of lithium ions from the positive electrode to the negative electrode during charging and vice versa during discharging. The positive electrode of a Li-ion battery consists of one of a number of lithium metal oxides, which can store lithium ions and the negative electrode of a Li-ion battery is a carbon electrode. The electrolyte is made up of lithium salts dissolved in organic carbonates. Lithium-ion batteries are very used in WECS due to their advantages as small self-discharge and long life for deep cycles.

6.2.1.9

Flow Batteries or Vanadium-Redox Flow Battery (VRB)

The vanadium-redox flow battery store energy in two tanks that are separated from the cell stack. There are three kinds of flow batteries: • Vanadium Redox (VR), • Polysulphide Bromide (PSB), • Zinc Bromine (ZnBr). The flow battery process is explained in Fig. 6.5. In flow batteries, energy is stored as a potential chemical energy and it is stored in the electrolyte solutions. The advantages of VRB are [3]: • Increasing in energy densities by more than 70% due to increased vanadium ion concentrations. • Operation at increased current densities. • Increasing the operating temperature window. • Storing of megawatts/ megawatt-hours of power and energy in simple designs. • Flexibility to design power and energy capacities separately. • Discharging power for up to 12 h at a time. • Quickly brought up to full power when needed. • Long cycle life (>5000 deep cycles) due to excellent electrochemical reversibility.

190

6 Modeling of Storage Energy Systems Used in WECS

+

-

Electrodes

Electrodes

Loads

Electrolyte tank

Pump

Electrolyte tank

Pump

Fig. 6.5 Flow battery process

• High energy efficiencies. • Uses no highly reactive or toxic substances, minimizing safety and environmental issues. • Sit idle for long periods of time without losing storage capacity. • Low maintenance cost. For all these advantages, the vanadium-redox flow battery (VRB) is an excellent candidate for wind applications.

6.2.2 Battery Electrochemical Model The simplest models are based solely on electrochemistry. These models can predict energy storage but they are not able to model phenomena such as the time rate of change of voltage under load nor do they include temperature and age effects. A cell is characterized by its capacity. It is an amount of electricity, expressed in Ah, and that it is able to return after a full charge, and discharged at a constant current. This capacity varies depending on several factors, such as the intensity of discharge, temperature, and electrolyte concentration.

6.2 Electrochemical Storage

191

The Peukert equation (Eq. 6.1), is an empirical formula which approximates how the available capacity of a battery changes according to the rate of discharge [3–21]. n ·t =C Ibatt

(6.1)

where I batt is the discharge current and n is the Peukert constant. The Peukert constant increases with age for any of the battery types (Table 6.1). t is the time to discharge at current I batt , C is the capacity according to Peukert, at a one-ampere discharge rate, expressed in Ah. Equation 6.2 shows that at higher currents, there is less available capacity in the battery. The Peukert constant is directly related to the internal resistance of the battery and indicates how well a battery performs under continuous heavy currents. We can relate the discharge current at one discharge rate to another combination of current and discharge rate. Then we obtain [21–32]  C1 = C2 ·

Ibatt2 Ibatt1

n−1 (6.2)

where C 1 and C 2 are the capacities of the battery at different discharge-rate states. The state of charge (SOC) at a constant discharge rate can be obtained by the following equation:  SOC(t) = 1 −

Ibatt C

 ·t

(6.3)

The current is continuously variable over time. We discredit then the above equation by considering the constant current between two calculation steps. We can determine the expression of the change in charge state of the cell at time t k : Ibatt SOC(tk ) = C1

 k

·

Ibatt k Ibatt1

n−1 · t

(6.4)

This approach also takes into account the phases of recharging the battery. Indeed, if the current in the cell becomes negative, its state of charge increases. Ultimately, cell state of charge expressed by SOC(tk ) = SOC(tk−1 ) + SOC(tk )

(6.5)

Table 6.1 Peukert constant AGM batteries

Gel batteries

Flooded batteries

Typical lead-acid batteries

Lithium-Ion batteries

1.05–1.15

1.1–1.25

1.2–1.6

1.35

1.1

192

6 Modeling of Storage Energy Systems Used in WECS

These models are modeling the batteries in the shape of electronic circuits. There are many models proposed by different scientists. An application is made under MATLAB/Simulink, using the CIEMAT model (Fig. 6.6). Some simulation results are presented in Fig. 6.7.

6.3 Hydrogen Energy Storage (HES) Generally, hydrogen system consists of an electrolyzer, a pressurized gas store, and fuel cells. The electrolyzer converts electrical energy into chemical energy in the form of hydrogen during times of surplus electrical supply. This hydrogen is stored until there is a shortage of electrical energy to power the loads on the system, and then it is reconverted by a fuel cell (hydrogen and air oxygen) to electricity. Hydrogen can store energy for long periods. Different hydrogen storage modes are used: • • • •

compressed, liquefied, metal hydride, etc.

For example for a wind system or a hybrid wind/photovoltaic (or hydro) system supplying a load (Fig. 6.8) We can add battery system for short-term storage and also to stabilize the system against fluctuations in energy sources, but for a long-term storage, an electrolyzer coupled to a hydrogen storage tank is used. The system management operates as Fig. 6.9. • When the energy demand load is less than the production of wind and solar panels, the excess energy is sent to the electrolyzer to produce the hydrogen and then store it. • When energy demand exceeds energy load capacity available, the stored hydrogen is regenerated into electricity via cell fuel. Different wind or hybrid system structures with hydrogen storage are proposed in scientific research and some of them are real implanted systems (Fig. 6.10). In wind energy conversion system, HES with all advantages (higher energy density and lower per volume than gasoline) is one of the best storage solutions to suppress fast wind power fluctuations.

6.3 Hydrogen Energy Storage (HES)

193

EDC1 To6 Ibat

EDC1 Ibat

1

Pbat

1

Ibat

Vbat

Pbat SOC SOC Vbat Vbat DOD DOD

2

Tamb Cbat

Tamb

Tamb

State of charge Voltage battery In1 In2

3

rd

rd In3

Subsystem

2 Cbat

1 Pbat 1 Ibat

Divide 2 Vbat

Interpreted MATLAB Fcn Qbat Clock 3 Divide1

Subtract1

DOD

1 3

Tamb

CT

Constant

2

Tamb

SOC Subtract

Ibat

Cbat

Divide2

4 Cbat

capacity

Fig. 6.6 CIEMAT model under MATLAB/Simulink

194

6 Modeling of Storage Energy Systems Used in WECS

Fig. 6.7 Simulation results

120 T 35 T 25 T 45

Cbatt(Ah)

100 80 60 40

0

2

4

6 Ibatt (A)

8

10

0.8

1

120 T 45 c° T 35 c° T 25 c°

DOD

100 80 60 40

0

0.2

0.4

0.6 Cbatt(Ah)

1

SOC

0.8

T 25 c° T 35 c° T 45 c°

0.6 0.4 0.2 0 40

60

80 Cbatt(Ah)

100

120

6.4 Mechanical Storage

195

SOC

Fig. 6.7 (continued)

DOD

Cbatt(Ah)

Cbatt(Ah)

6.4 Mechanical Storage 6.4.1 Flywheel Energy Storage (FES) Flywheel electric energy storage system includes a cylinder with a shaft connected to an electrical generator. Electric energy is converted by the generator to kinetic energy which is stored by increasing the flywheel’s rotational speed. The stored energy is converted to electric energy via the generator, slowing the flywheel’s rotational speed (Fig. 6.11). For wind stand-alone applications storage cost still represents the major economic restraint. Energy storage in wind systems can be achieved in different ways. However, the inertial energy storage adapts well to sudden changes of the power from the wind generator. Moreover, it allows obtaining high power to weight and number of charge cycles and discharge very high.

196 Fig. 6.8 Hybrid wind/ photovoltaic system with hydrogen storage supplying a load

6 Modeling of Storage Energy Systems Used in WECS

Battery System

PV or Hydro Loads Wind

Fuel Cell Electrolyser

Hydrogen storage

The reference speed for the flywheel is determined by Oubelaid et al. [33]:  Ωref =

2 · E c ref Jt

(6.6)

With Jt = JI G + JFlywheel

(6.7)

The reference speed is limited in order to maintain the IG in the area of operation at constant power and not exceed the maximum speed of the flywheel. Figure 6.12 represents the torque and power as a function of speed. We notice that: • For 0 ≤ Ω ≤ Ωrated , the torque may be maximal giving up a power proportional to the speed PI G = k · Ω. • For Ω  Ωrated , the power is maximum and corresponds to the rated power of the machine, the electromagnetic torque is inversely proportional to the speed Tem = k .

6.4 Mechanical Storage

197

Fig. 6.9 Energy management of a wind/PV system with hydrogen storage

So, if we want to have the machine-rated power, it is necessary to use it beyond its rated speed, which lets us consider the speed as the lower limit storage and the dual value of speed as the upper limit storage. Thus, a field weakening operation will be necessary to obtain a constant power in the speed range of 1500–3000 rpm. The reference flux is then determined by  Φref =

Φrated Φrated ·

Ωrated |Ω|

⇒ ⇒

i f |Ω| ≤ Ωrated i f |Ω|  Ωrated

(6.8)

With Ω flywheel speed, Ωrated : rated speed, Φrated : rated flux and Φref : Reference flux. In wind energy conversion system, FES is able to suppress fast wind power fluctuations. We make an application to a WECS based on induction generator. The system is constituted of a wind turbine, an induction generator, a rectifier/inverter, and a flywheel energy storage system. The goal of the device is to provide a constant power and voltage to the load connected to the rectifier/inverter even if the speed varies. This can be achieved mainly by the control of the DC bus voltage at a constant

198

6 Modeling of Storage Energy Systems Used in WECS

Grid

PV or Hydro

Wind

Electricity

Residential standalone system

Heat

Fuel Cell Electrolyser

Water

Hydrogen storage

Heat

Hydrogen refueling Hydrogen compression Hydrogen distribution

Fig. 6.10 Production of electricity and heat of a hybrid wind/PV(or hydro) system with hydrogen storage

value and the flywheel energy storage system participates to maintain the power of the load constant as long as the wind power is sufficient. To control the speed of the flywheel energy storage system, we must find a reference speed with which the system must turn to ensure the energy transfer required at each time. The reference speed can be determined by the reference energy. The power assessment of the overall system is given by Rekioua et al. [34]: Pref = Pload − Pwind − P

(6.9)

where Pref is the reference power, Pload is the load power, Pwind is the wind power, and P is the power required to control the DC voltage Vdc at a constant value.

6.4 Mechanical Storage

199

Fig. 6.11 Flywheel system. a For long term (more than one hour). b For short term ( 0

(7.8)

  w = ω˙ topt + a0 ωtopt − ωt

(7.9)

Tg = Ta − K t ωt − a0 Jt εω − Jt ω˙ topt

(7.10)

Similarly, we obtain the expression

Hence the control:

The choice of the dynamic of the tracking error of the first order is due to the fact that the relative degree of the system is equal to 1.

218

7 Overview of Control Methods Used in WECSs

This control by static state feedback leads to good feedback in the lack of disturbance, but if not, it has the disadvantage of not rejecting these disturbances. We make an application under MATLAB/Simulink. The simulation was performed under the following conditions: • Lack of additive disturbance to the generator torque • A profile of an average wind velocity of 7 m/s. The characteristics of the controlled wind turbine are shown in Fig. 5.1, which means the wind speed, the aerodynamic torque, rotor speed, and its optimal reference, the aerodynamic power and its optimal reference, the torque/couple of the generator. We note that the rotor speed is very close to its optimal value. The wind energy capture is maximal in the range of 0 to 150 s. The trajectory of the aerodynamic power is almost identical to the one of its reference then, we can confirm that this control method gives good feedback in the lack of disturbances.

7.2.2 Nonlinear Dynamic Control by State Feedback (NLDCSF) Nonlinear dynamic control by state feedback is a control technique that utilizes a nonlinear control law and state feedback to regulate the system states of a Wind Energy Conversion System (WECS). Unlike linear control approaches, which assume the linearity of the system dynamics, nonlinear dynamic control accounts for the nonlinearities inherent in the WECS. This control is developed with the objective to reject the constant additive disturbances on the control. We apply a second-order dynamic on the tracking error formula. εω = ωtopt − ωt ε¨ ω + b1 ε˙ ω + b0 εω = 0

(7.11)

b0 et b1 are selected such as the polynomial s 2 + b1 s + b0 = 0 is Hurwitz. Assuming also a constant disturbance acts on the system, we have Jt ω˙ t = Ta − K t ωt − Tg + d

(7.12)

With deriving this equation, we obtain ω¨ t =

 1 ˙ Ta − K t ω˙ t − T˙g Jt

(7.13)

We are looking for a control that will change the system to a double integrator with the new input w ω¨ t = w

(7.14)

7.2 Level 1 (Mechanical Part)

219

Then we deduce  T˙g = Jt

1 ˙ Kt Ta − ω˙ t − w Jt Jt

 (7.15)

We use Eqs. 7.11 and 7.13, we obtain then:     w = ω¨ topt + b1 ω˙ topt − ω˙ t + b0 ωtopt − ωt

(7.16)

Finally, we obtain the dynamic control: T˙g = T˙a + (b1 Jt − K t ) ω˙ t + b0 Jt ωt   − Jt ω¨ topt + b1 ω˙ topt + b0 ωtopt

(7.17)

The controlled wind turbine characteristics are shown in Fig. 7.2. The compromise between the optimization of wind energy capture and the minimization of the transitional efforts undergone by the driving device is accomplished by choosing a tracking dynamic which permits to follow of the average trend of the optimal rotation speed without following closely the peaks of wind. We make an application under MATLAB/Simulink with the same conditions in No linear control by static state feedback. The controlled wind turbine characteristics are shown in Fig. 7.3. We note that the rotor speed increases with the wind speed. The wind speed trajectory is almost identical to the one of its reference. The captured aerodynamic power is maximal for the wind high speeds, and it is noted too that it coincides with the optimal power.

7.2.3 Indirect Speed Control (ISV) Indirect speed control is commonly used in wind turbine systems to regulate the rotor speed, ensuring optimal power generation and safe operation. It is often combined with other control strategies, such as pitch control or power regulation, to achieve desired performance objectives. The wind system, under certain conditions, is dynamically stable around of any balance point of the maximal efficiency curve for a generator torque and a constant operating wind speed. The maximal aerodynamic   efficiency curve is defined in the plane (ωt , Ta ) by the set of points ωtopt , Taopt corresponding to the interval of wind speeds in which the wind turbine operates. We have Ta =

1 2 ρ π R 3 Cq (λopt ) νwind 2

(7.18)

220

7 Overview of Control Methods Used in WECSs

Fig. 7.2 Obtained results of nonlinear control with static feedback

If the aerodynamic torque is controlled so as to follow the optimal torque, the wind turbine remains around yield curve.  its optimal  We have a given point λ0 , C p0 of the curve C p (λ) that we want to follow. In order to maximize the energy production below the nominal power Pnom , this point is  selected in a neighborhood where power coefficient is at its maximum λopt , C popt0 . Since that:

7.2 Level 1 (Mechanical Part)

221

Fig. 7.3 Obtained results of the nonlinear control by dynamic state feedback

Cq (λ) =

C p (λ) λ

(7.19)

So the aerodynamic torque could be written Ta =

C p (λ) 2 1 ρ π R3 νwind 2 λ

(7.20)

If λ = λopt , Ta could be expressed according to the rotor Ta =

1 1 ρ π R 5 C popt 3 ωt2 2 λopt

(7.21)

This torque corresponds to an optimal operating in relation to the wind speed and it is to square of the aero-turbine rotation speed at the operating point  proportional  λopt , C popt0 .

222

7 Overview of Control Methods Used in WECSs

Ta = kopt ωt2

(7.22)

With: kopt =

1 ρ π R 5 C popt 3 2 λopt

(7.23)

If we consider the model with one mass of wind turbine, in steady we have 0 = Ta − K t ωt − Tg

(7.24)

The generator torque Tg satisfies Tg = kopt ωt2 − K t ωt

(7.25)

The structure of the indirect control speed is shown in Fig. 7.4. This technique has two disadvantages: The first is that it does not consider sufficiently the dynamic aspects of the aero-turbine and the wind. Indeed, as the synthesis of this control assumes that the wind turbine is in a steady state on the optimal efficiency curve. The rotor speed fluctuations in response to the wind variations deviate in a significant way from the wind turbine from this trajectory. In addition, the wind speed variations are faster than the closed loop dynamic system, the control system does not have the time to stabilize on the optimal efficiency curve. This continuous transaction is accompanied by energy losses. The second disadvantage is the lack of robustness toward the measurement noise and disturbances.

Fig. 7.4 Indirect control speed

7.2 Level 1 (Mechanical Part)

223

Fig. 7.5 Obtained results by indirect control speed

We make an application under MATLAB/Simulink. The simulation is performed under the same conditions as for the previous two methods. We observe from Fig. 7.5a that the rotor speed deviates from its optimal trajectory even in the lack of disturbance. The aerodynamic power Fig. 7.5d is consequently affected by this deviation.

7.2.4 Comparison Between the Three Controls Table 7.1 provides a comparison between state feedback control, nonlinear control by static state feedback, and indirect speed control used in Wind Energy Conversion Systems (WECS). To highlight the most effective controls, we make a comparison between the three methods under two different perturbation torque.

224

7 Overview of Control Methods Used in WECSs

Table 7.1 Comparison of the three different control methods Aspects

State feedback control Nonlinear control by static state feedback

Indirect speed control

Control objective

Regulate system states Regulate system states Regulate rotor speed

Control law design

Linear state feedback control

Nonlinear state feedback control

PI or PID control

System modeling requirements

Linear system model required

Nonlinear system model required

Linear system model

Feedback signal

Measured system states

Measured system states

Measured rotor speed

Control inputs

Actuators directly influenced by control signals

Actuators directly influenced by control signals

Actuators directly influenced by control signals

Stability

Stable if system is stable and linear

Stability analysis may Stability analysis be more challenging required

Performance

Effective for linear systems

Improved performance for nonlinear systems

Good performance for steady-state conditions

Complexity

Relatively simple

More complex design and analysis required

Relatively simple

Flexibility

Limited to linear systems

Can handle nonlinear system behavior

Limited to steady-state conditions

Robustness

Limited robustness to model uncertainties

Improved robustness Moderate robustness to to model uncertainties model uncertainties

Adaptability

Limited adaptability to changing operating conditions

Limited adaptability to changing operating conditions

Moderate adaptability to changing wind conditions

Implementation Complexity

Relatively simple

More advanced control design and analysis required

Relatively simple

Application in WECS

Suitable for linear wind turbine systems

Can handle nonlinearities in wind turbine systems

Suitable for controlling rotor speed

Dependencies

Relies on accurate state measurements and linear system behavior

Relies on accurate state measurements and knowledge of nonlinear system behavior

Relies on accurate measurement of rotor speed

Overall performance

Effective for linear systems

Can achieve improved Good performance for performance in steady-state conditions nonlinear systems

7.3 Level 2 (Electrical Part)

225

• For Tr = 10 kN.m

Static error on Pa (kW) Static error on ωt (tr/min)

ISV

NLCSSF

NLDCSF

14.7

2.9

0

0.47

0

ISV

NLCSSF

NLDCSF

22.2

4.15

0

0.72

0

2.97

• For Tr = 15 kN.m

Static error on Pa (kW) Static error on ωt (tr/min)

4.22

We can remark in the studied example, that the most effective strategy is the no linear dynamic control by state feedback (NLDCSF). It rejects the disturbance on the control and achieves better tracking of the optimal speed.

7.3 Level 2 (Electrical Part) 7.3.1 Scalar Control of Wind System (SCWS) Scalar control in wind systems refers to a control strategy that focuses on regulating a single scalar variable, such as power output or rotor speed, to achieve desired performance and operational objectives. Unlike more complex control strategies that consider multiple system states and variables, scalar control simplifies the control structure by prioritizing the control of a single variable. The steady-state performance of an induction motor is modeled using the conventional equivalent circuit (Fig. 7.6). Assuming that Rm is infinite, the expression of electromagnetic can be written as  2 V Tem AC = 3P. s

ωs Rr Rs g

ωs X m2

2 2  − ωs2 . L s L r − X m2 + ωs2 RrgL s + Rs L r Rr g

(7.26)

With: g=

ωs − ω ωs

(7.27)

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7 Overview of Control Methods Used in WECSs

Rs

Xs

Vs

Xr

Xm

Rr/g

Rm Rs

Fig. 7.6 Induction motor equivalent circuit

Vs rms motor voltage (V).Rs stator resistance per phase (), Rr equivalent rotor resistance per phase (), Rm core loss resistance (), Xs stator leakage reactance (), Xr equivalent rotor leakage reactance (), Xm magnetizing reactance (), ωs angular frequency of the supply (rd/s), ω motor speed (rd/s), and g slip. Mechanical power Pmec , stator current Is and stator power Ps will be Pmec = 3

Rr 2 I g r

(7.28)

Rr /g + j L r ωs I s = 1 + Ir j L m ωs

(7.29)

Ps = Pmec + P jr + P js

(7.30)

From the three previous equations, we obtain the total power absorbed by the stator according to speed: ⎛ ⎜ (ωs − ω).ωs Ps = 3k 2 ⎝  2 + Rs Rr XLms



⎞

Rr2

+

L r2 .(ωs (Rr L s )

− ω) 2

2

⎟ ⎠

(7.31)

Generally, in variable speed drives, motor air-gap flux is maintained constant at all frequencies so that the motor can deliver a constant torque. This will occur if the Vs /fs (Vs /ωs ) ratio is kept constant at its nominal value. To compensate the voltage drop due to stator resistance effect at low frequencies, a boost voltage Vs0 is added to phase voltages (Fig. 7.7). For aerodynamic loads, the stator voltage as function of frequency is given by For 0 ≤ f ≤ f N

Vs = Vs0 + K f˙N

(7.32)

7.3 Level 2 (Electrical Part)

227

V

Vs

VN P=cste Tem.ws2=cste VS/fs=cste

Vs0

Tem

0

fN

N

f(Hz)

Fig. 7.7 Scalar control

For f ≥ f N

Vs = VN

(7.33)

7.3.2 Vector Control of Wind System (VCWS) Vector control, also known as field-oriented control (FOC) is an advanced control strategy used in wind systems to achieve precise control of the generator rotor flux and torque. It enables decoupled control of the machine’s magnetic flux and torque components, allowing for improved dynamic response and efficiency (Fig. 7.8). We oriented the rotor flux Fr along the direct axis 

dr = r qr = 0

We obtain

(7.34)

228

7 Overview of Control Methods Used in WECSs

-eds Vds*

Ids

Vds IM Vqs

Vqs*

Iqs

+eqs

Fig. 7.8 Structure of the decoupling regulator

⎧ L m d r d Ids ⎪ ⎪ Vds = Rs Ids + σ L s + − ωs .σ L s .Iqs ⎪ ⎪ ⎪ dt L r dt ⎪ ⎪ ⎪ ⎪ d Iqs Lm ⎪ ⎪ r + ωs .σ L s .Ids + ωs ⎨ Vqs = Rs Iqs + σ L s dt Lr L m Iqs ⎪ ⎪ ⎪ ωr AC = ⎪ ⎪ Tr r ⎪ ⎪ ⎪ ⎪ Lm ⎪ ⎪ ⎩ Tem AC = p ( r .Iqs ) Lr

(7.35)

The new control is as follows: ⎧ ⎪ ⎨ Vds ∗ = (Rs + s.σ L s )Ids = Vds + ωs .σ L s .Iqs = Vds + eds  Lm ⎪ Vqs ∗ = (Rs + s.σ L s )Iqs = Vqs − ωs r + ωs .σ L s .Ids = Vqs − eqs ⎩ Lr (7.36)

7.3.3 Direct Torque Control of Wind System (DTCWS) 7.3.3.1

DTC Principals

Direct torque control (D.T.C) of induction machines (I.M) is a powerful control method for motor drives. Featuring a direct control of the stator flux and torque instead of the conventional current control technique, it provides a systematic solution

7.3 Level 2 (Electrical Part)

229

to improve operating characteristics of the motor and the voltage inverter source [1– 10]. In principle, D.T.C method is based mainly on instantaneous space-vector theory. By optimal selection of the space voltage vectors in each sampling period, D.T.C achieves effective control of the stator flux and torque. Consequently, the number of space voltage vectors and switching frequency directly influence the performance of D.T.C control system. For a prefixed switching strategy, the drive operation, in terms of torque, switching frequency, and torque response, is quite different at low and high speed [11, 12].

7.3.3.2

DTC Structure

A configuration of D.T.C scheme is represented in Fig. 5.9. In this system the instantaneous values of flux and torque can be calculated from stator variables and mechanical speed or using only stator variables. Stator flux and torque can be controlled directly and independently by properly selecting the inverter switching configurations. With a three-phase voltage source inverter, six non-zero voltage vectors and two zero-voltage vectors can be applied to the machine terminals. The stator flux can be estimated using measured current and voltage vectors [13– 15]: t ϕs (t) =

(Vs − Rs i s )dt

(7.37)

0

Since stator resistance Rs is relatively small, the voltage drop Rs is might be neglected (Vs >> Rs i s ), we obtain: ϕs (t) = V s.T + ϕs (0)

(7.38)

ϕs (0) is the stator flux initial value at the switching time, and T is the sampling period in which the voltage vector is applied to stator windings. It is clear that stator flux directly depended on the space voltage vector Vs and the system sampling period T. The stator voltage vector Vs is selected using Table 5.1, where signs of torque and flux errors ET and E are determined with a zero hysteresis band (Fig. 7.9).

where

E T = Ter e f − Tem AC

(7.39)

E ϕ = ϕsr e f − ϕs

(7.40)

230

7 Overview of Control Methods Used in WECSs

Fig. 7.9 Block diagram of the conventional D.T.C of induction motor drives

ϕs =



(ϕsα )2 + (ϕsβ )2

(7.41)

Table 7.2 shows the associated inverter switching states of the conventional direct torque control strategy. The definition of flux sector and inverter voltage vectors are shown in Fig. 7.10, where the stator flux vector is rotating with a speed of ωrAC . For each possible switching configuration, the output voltages can be represented in terms of space vector, according to the following equation:  VS = VSα + j VSβ =

2 4π [Va + Vb ex p( j2π over 3) + Vc ex p( j )] 3 3

(7.42)

where Va , Vb, and Vc are voltage phases. Table 7.2 Switching table for the conventional DTC ET

Eφ

N 1

2

3

4

5

6

ET = 1

Eφ = 1

V2 (110)

V3 (010)

V4 (011)

V5 (001)

V6 (101)

V1 (100)

Eφ = 0

V6 (101)

V1 (100)

V2 (110)

V3 (010)

V4 (011)

V5 (001)

ET = 0

Eφ = 1

V3 (010)

V4 (011)

V5 (001)

V6 (101)

V1 (100)

V2 (110)

Eφ = 0

V5 (001)

V6 (101)

V1 (100)

V2 (110)

V3 (010)

V4 (011)

7.3 Level 2 (Electrical Part)

231

Fig. 7.10 Movement of the inverter voltage in the space-vector plane

7.3.3.3

Application: DTC of IM Fed by a Wind Turbine

The DTC control applied to the induction motor is simulated through MATLAB/ Simulink. The simulation model of the induction motor is given in Fig. 7.11. Simulation diagram of the stator flow control stator is illustrated in Fig. 7.12. The control signal flow (fx_con) is generated by a comparator with hysteresis, after estimating the vector flux and compares the flow module to its reference value (Fig. 7.13). Figure 7.14 shows the bloc diagram of the overall system. Some simulation results are presented (Fig. 7.15).

Fig. 7.11 Simulation diagram of induction motor

232

7 Overview of Control Methods Used in WECSs

Fig. 7.12 Simulation diagram of stator flux control

Fig. 7.13 Simulation diagram of the electromagnetic torque control

Fig. 7.14 Simulation diagram of the overall structure of the DTC control

7.3 Level 2 (Electrical Part)

233

Reference Torque Speed

Torque

1000 tr/mn/div 0.5 Nm/div

Fig. 7.15 Evolution of the torque, speed, stator current, and stator flux with a speed reference

7.3.4 Modulated Hysteresis Direct Torque Control of Wind System (MHDTCWS) 7.3.4.1

MHDTC Control Strategy

The use of hysteresis controllers in DTC leads to distortion in the currents supplied to the grid and large variations in the power factor. On another hand, when using a vector control strategy, the direct bus voltage varies with the wind variations but the associated PWM leads to a better quality of current and power factor. So, in order to obtain performances with the advantages of both DTC and FOC methods, we apply an improved modulated hysteresis direct torque control which consists in superposing, to the constant torque reference, a triangular signal with the desired switching frequency as in the PWM control used with FOC. The modulated reference torque is compared to the estimated torque by using a hysteresis controller as in the classical DTC (Fig. 7.16). The new torque reference T* eref is then defined by [16–19]

234

7 Overview of Control Methods Used in WECSs

Fig. 7.16 Basic concept of the modulated hysteresis

Ter∗ e f = Ter e f + Tetr

(7.43)

where T eref is the reference torque and T etr is the triangular signal. To impose a switching frequency, the estimated torque variation during a half period should not exceed the difference between the maximum of the upper limit and the minimum of the lower limit. In this case, the switching frequency is equal to the imposed one only if the triangular signal magnitude Atr and the hysteresis bandwidth B H verify the two conditions: 

dTe = 4 f tr (Atr + B H ) dt max  dTe 2B H > dt min Ttr

(7.44) (7.45)

where T e is electromagnetic torque and f tr is the frequency of the triangular signal The torque dynamic (dT e /dt) is equivalent to the current dynamic and so it is fixed by the machine parameters (Fig. 7.17). The modulated hysteresis application needs to know the two values (dT e /dt)min and (dT e /dt)max to obtain the minimal triangular frequency which is expressed by  f tr min =

dTe dt



1 max 4(Atr + B H )imp

(7.46)

7.3 Level 2 (Electrical Part)

235

Fig. 7.17 Determination of the switches states

7.3.4.2

Application to Wind Energy Conversion System

We make an application of MHDTC on WECS. The studied system is represented in the following figure (Fig. 7.18). The control principle consists in adjusting the active power supplied to the grid to its reference value Pr e f and the reactive power Q r e f to zero in order to fix the power factor at the unit. The active power reference is deduced by controlling the direct bus voltage with a proportional integral corrector generating the current reference i c−r e f to the capacitance.

Fig. 7.18 Wind generator based on an induction generator

236

7 Overview of Control Methods Used in WECSs

Thus, we can write   Pr e f = Vdc . i dc − i cr e f

(7.47)

Pr e f = Pdc − Pcr e f

(7.48)

i cr e f = P I (Vdcr e f − Vdc )

(7.49)

With

Active and reactive power references Pref and Qref are given by the following equations: Pr e f = E d i nd_r e f + E q i nq_r e f

(7.50)

Q r e f = E q i nd_r e f − E d i nq_r e f

(7.51)

where E d , E q are the Park transform of the stator emfs E 1 , E 2 , E 3 Equation 7.50 is then multiplied by E d and Eq. 7.51 by E q . Then, the addition and subtraction of the two new equations give the reference current values according to active and reactive power ones by (Fig. 7.19) i nd−r e f =

Pr e f E d + Q r e f E q E d2 + E q2

(7.52)

i nq−r e f =

Pr e f E q − Q r e f E d Vd2 + Vq2

(7.58)

where Vd and Vq are the Park transform of the three-phase stator voltages. We make application under MATLAB/Simulink. The model of power control is given as (Fig. 7.20): Some simulation results can be represented (Fig. 7.21). This method uses the advantages of the classical DTC and the FOC while correcting some of their difficulties. It leads to: • a sinusoidal grid current with constant periodic frequency, • a unit power factor, • a constant DC bus voltage.

7.3 Level 2 (Electrical Part)

Fig. 7.19 Power control

Fig. 7.20 Bloc diagram of power control under MATLAB/Simulink

237

238

7 Overview of Control Methods Used in WECSs

Fig. 7.21 Simulation results of MHDTC applied to an induction generator

7.3 Level 2 (Electrical Part)

239

Fig. 7.22 PI controller

7.3.5 Direct Power Control of Wind System (DPCWS) 7.3.5.1

Direct Power Control (DPC) Principals

In this control strategy, the error in the reference power and the actual power is utilized to generate the voltage control directly as in conventional DTC drives [20]. This method reduces the number of PI controllers used when compared to the vector control-based variable speed wind turbine generator systems. The DPC like DTC is a stator flux-based control technique having the advantages of robustness and fast controls.

7.3.5.2

DPC Structure

In the DPC structure, we use a PI controller in the DC link, this allows us to reduce the DC link capacitor fluctuation voltages (Fig. 7.22).

7.3.5.3

Application: DPC of IM Fed by a Wind Turbine

Figure 7.23 shows the configuration of the proposed control system based on the DPC method. The controller features relay control of the active and reactive power by using hysteresis comparators and a switching table. In this configuration, the DC bus voltage is regulated by adjusting the active power transmitted to the load. As shown in Fig. 7.23, the active power control Pref is provided from the PI regulator of the DC voltage controller bloc. The reactive power control Qref is directly given from the outside of the controller. Errors between the controlled and the estimated

240

7 Overview of Control Methods Used in WECSs

Fig. 7.23 Block scheme of DPC

feedback power are input to the hysteresis comparators and digitized to the signals P and Q defined as [20–28]: P = 1 i f Pr e f − P ≥ h p , P = 0 i f Pr e f − P ≤ −h p Q = 1 i f Q r e f − Q ≥ h q , Q = 0 i f Q r e f − Q ≤ −h q

(7.59)

where hp and hq are the hysteresis band. Also, the phase of the flux vector is converted to the digitized signals θn . For this purpose, the stationary coordinates are divided into twelve (12) sectors, as shown in Fig. 7.24 and the sectors can be numerically expressed as. The digitized variables P, Q and the flux vector position γψ = arctg(ψβ /ψα ) form a digital word, which by accessing the address of the lookup table selects the appropriate voltage vector according to the switching Table 7.3.

7.3.6 Sliding Mode Control (SMC) The sliding mode control can be justified and designed using the stability notion of Lyapunov [21]. Whatever the application, the design of the sliding mode can be summarized in three steps: • The choice of the number of the sliding surface. Generally this is later equal to the input control vector.

7.3 Level 2 (Electrical Part)

241

Fig. 7.24 α-β plane divided into twelve sectors to detect the phase of the voltage vector

Table 7.3 Switching table for direct instantaneous power control P

Q

1

2

3

4

5

6

7

8

9

10

11

12

0

1

101

111

100

000

110

111

010

000

011

111

001

000

0

0

111

111

000

000

111

111

000

000

111

111

000

000

1

1

101

100

100

110

110

010

010

011

011

001

001

101

1

0

100

110

110

010

010

011

011

001

001

101

101

100

• The choice of the sliding surface equation form. It must satisfy the convergence of the control and the stability of the system. This goal can be reached if the control ˙ < 0. Based on variable U c permits it to satisfy the Lyapunov function S(x) S(x) this condition, Slotine proposes a general form of sliding mode surface, [21]:  S(x) =

δ + λx dt

r −1

(x ∗ − x)

(7.60)

• The control law design. The control variable is decomposed into two parts; U eq and U n : Uc = Ueq + Un

(7.61)

The dynamic while in sliding mode can be written as: S˙ = 0. By solving this equation, the equivalent control U eq can be obtained [21]

242

7 Overview of Control Methods Used in WECSs

˙ The nonlinear component U n satisfies S(x) S(x) < 0 and is given by Un = K .sign(S(x))

(7.62)

The commonly used form of U n is a constant relay control (Fig. 7.25), [21]. However, this latter produces a drawback in the performances of a control system, which is known as the chattering phenomenon. In order to reduce the chattering phenomenon due to the discontinuous nature of the controller, a smooth function is defined in some neighborhood of the sliding surface with a threshold (Fig. 7.26) [21]. This part consists of calculating the two components of control equivalent and nonlinear of the control variables by an adequate surface [21]. In this case we chose the error between the variables of control like a simple surface of sliding mode. For the mechanical speed, the surface is given by S(ωm ) = ωm − ωm_r e f

(7.63)

Fig. 7.25 Relay function Un

+K

S(x)

-K

Fig. 7.26 Smoothed sign function

Un

+K

- +

-K

S(x)

7.3 Level 2 (Electrical Part)

243

The first derivate of (Eq. 7.63) gives ˙ S(ω) = ω˙ − ω˙ r e f

(7.64)

In the sliding mode regime arise, the dynamic of the system in sliding mode is subjected to the following equation S(ψ) = 0 thus for the ideal sliding mode we ˙ = 0 [21]. have also S(ψ) With the conditions of the sliding mode, the Eq. 7.64 will be written as: Tem_eq = Tgear − J.ω˙ r e f

(7.65)

where Tem_eq is the equivalent component of the reference electromagnetic torque. The reference electromagnetic torque is defined by Tem_r e f = Tem_eq + Tem_n

(7.66)

The nonlinear component of electromagnetic torque Tem_n satisfies the Lyapunov ˙ < 0 and is given by a relay or smoothed function (Figs. 7.25 condition S(x) S(x) and 7.26). The synoptic scheme of the sliding mode control strategy is given in Fig. 7.27.

Fig. 7.27 Synoptic scheme of sliding mode control strategy

244

7 Overview of Control Methods Used in WECSs

Fig. 7.28 Basic scheme of electromagnetic torque fuzzy controller

7.3.7 Fuzzy Logic Controller (FLC) Classic controllers PI are replaced by fuzzy controllers for controlling the stator flow and the electromagnetic torque of the electrical generator. These controllers are based on the same structure of a conventional controller with the difference that we retain the incremental form. The following figure shows the basic idea of the electromagnetic torque controller (Fig. 7.28). With: E is the error defined as E(k) = Te−r e f (k) − Te k

(7.67)

dE: is the error derivate and is given as d E(k) =

E(k) − E(k − 1) Te

(7.68)

The regulator output is Isq−r e f (k) = Isq−r e f (k − 1) + d Isq (k)

(7.69)

The fuzzy rules used are listed in Table 7.4. Table 7.5 gives a comparison between scalar control, field-oriented control (FOC), direct torque control (DTC), modulated DTC, sliding mode control, and fuzzy logic control (FLC) in wind systems.

7.4 Conclusion

245

Table 7.4 Fuzzy rules table d Isq E

dE GN

PN

Z

PP

GP

GN

GN

GN

PN

PN

Z

PN

GN

PN

PN

Z

PP

Z

GN

PN

Z

PP

GP

PP

PN

Z

PP

PP

GP

GP

Z

PP

PP

GP

GP

Table 7.5 Comparison of the most used control used in WECS Control method

Description

Advantages

Drawbacks

Scalar Control (SC)

Simple control strategy based on a fixed operating point

Easy implementation, low computational requirements

Limited control precision, poor dynamic response

Field-Oriented Control (FOC)

Control strategy that aligns stator currents with rotor flux

Precise control of torque Complex control and flux, high dynamic algorithm, sensitive to response parameter variations

Direct Torque Control (DTC)

Control strategy that directly controls torque and flux without coordinate transformation

Fast dynamic response, High torque and flux accurate torque, and flux ripple, more complex control implementation

Modulated DTC (MDTC)

Improvement of DTC Reduced torque and flux Increased with reduced torque and ripple, improved computational flux ripple performance requirements, complex modulation techniques

Sliding Mode Control (SMC)

Control strategy based on reaching and maintaining a sliding surface

Robust against High control parameter variations and complexity, potential disturbances chattering phenomenon

Fuzzy Logic Control (FLC)

Control strategy using fuzzy inference for decision-making

Robust control in the presence of uncertainties, adaptability

Complex tuning of fuzzy membership functions, potential rule explosion

7.4 Conclusion In this chapter, various nonlinear control methods for wind turbine systems have been explored, focusing on both mechanical and electrical aspects. Specifically, three applications were demonstrated using MATLAB/Simulink: nonlinear control by static state feedback, nonlinear dynamic control by state feedback, and indirect speed control. After analyzing the examples, it was found that the most effective strategy among these was the nonlinear dynamic control by state feedback (NLDCSF). This

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7 Overview of Control Methods Used in WECSs

method proved to be highly effective in mitigating disturbances in the control system and demonstrated superior tracking of the optimal speed. By utilizing state feedback techniques, it was able to provide robust control performance and achieve better overall system performance. These findings highlight the significance of nonlinear control approaches, particularly the dynamic control by state feedback, in enhancing the performance and stability of wind turbine systems. Further research and development in this area can lead to improved control strategies for maximizing the efficiency and reliability of wind energy conversion systems.

References 1. Singh GK (2004) Self-excited induction generator research—a survey. Electr Power Syst Res 69(2–3):107–114 2. Tapia A, Tapia G, Ostolaza JX (2004) Reactive power control of wind farms for voltage control applications. Renew Energy 29(3):377–392 3. Valtchev V, Van den Bossche A, Ghijselen J, Melkebeek J (2000) Autonomous renewable energy conversion system. Renew Energy 19(1–2):259–275 4. Oubelaid A, et al (2023) Intelligent control of PMSM-driven electric vehicles using fuzzy logic and genetic algorithm. In: 2023 1st international conference on Circuits, Power and Intelligent Systems (CCPIS). Bhubaneswar, India, pp 01–06. https://doi.org/10.1109/CCPIS59145.2023. 10291544 5. Djurovic M, Joksimovic G (1996) Optimal performance of double fed induction generator in windmills. Renew Energy 9(1–4):862–865 6. Abdolghani N, Milimonfared J, Gharehpetian GB, Choi SS, Larkin R (1995) A Direct torque control method for CSC based PMSG wind energy conversion systems, performance of an autonomous diesel-wind turbine power system. Electr Power Syst Res 33(2):87–99 7. Noguchi T, Tomiki H, Kondo S, Takahashi I (1998) Direct power control of PWM converter without power-source voltage sensors. IEEE Trans Ind Appl 34:473–479 8. Kakouche K, Rekioua T, Mezani S, Oubelaid A, Rekioua D, Blazek V, Prokop L, Misak S, Bajaj M, Ghoneim SSM (2022) Model predictive direct torque control and fuzzy logic energy management for multi power source electric vehicles. Sensors 22(15):5669 9. Eyimaya SE, Altin N, Bal G (2021) Design of an energy management system with power fluctuation mitigation capability for PV/wind hybrid renewable energy sources. In: 2021 13th international conference on Electronics, Computers and Artificial Intelligence (ECAI). Pitesti, Romania, pp 1–6. https://doi.org/10.1109/ECAI52376.2021.9515083 10. Hansen S, Malinowski M, Blaabjerg F, Kazmierkowski MP (2000) Control strategies for PWM rectifiers without line voltage sensors. Proc IEEE APEC 2:832–839 11. Bin Abul Kashem S, Chowdhury MEH, Khandakar A, Shabrin N (2020) Wind power integration with smart grid and storage system: prospects and limitations. Int J Adv Comput Sci Appl 11(5):552–569 12. Rekioua D, Rekioua T, Laporte B, Benmahammed K (2001) Design of a position sensor for torque ripple minimization of a voltage source inverter fed self-synchronous machine. Int J Electron 88(8):939–953 13. Ojaghi M, Faiz J (2008) Extension to multiple coupled circuit modeling of induction machines to include variable degrees of saturation effects. IEEE Trans Magn 44(11):4053–4056. https:// doi.org/10.1109/TMAG.2008.2002405 14. Amrouche SO, Rekioua D, Rekioua T, Bacha S (2016) Overview of energy storage in renewable energy systems. Int J Hydrog 41(45):20914–20927 15. Tamalouzt S, Benyahia N, Rekioua T, Rekioua D, Abdessemed R (2015) Wind turbine-DFIG/ photovoltaic/fuel cell hybrid power sources system associated with hydrogen storage energy

References

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for micro-grid applications. In: 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). Marrakech, Morocco, pp 1–6. https://doi.org/10.1109/IRSEC.2015.745 5060 Hassani H, Rekioua D, Zaouche F, Rekioua T, Bacha S (2019) Sizing of standalone photovoltaic/ battery without and with hydrogen production systems. In: 2019 7th international renewable and sustainable energy conference (IRSEC). Agadir, Morocco, pp 1–6. https://doi.org/10.1109/ IRSEC48032.2019.9078313 Boukhezzar B, Siguerdidjane H (2005) Nonlinear control of variable speed wind turbines without wind speed measurement. In: Proceedings of the 44th IEEE conference on decision and control, and the European control conference 2005, Spain .Cámara EM, Macías EJ (2010) InTech book chapter 2. Wind Power Book Aissou R, Rekioua T, Rekioua D, Tounzi A, Application of nonlinear predictive control for charging the battery using wind energy with permanent magnet synchronous generator. Int J Hydrog Energy 41(45):20964–20973 Malinowski M, Kazmierkowski MP, Trzynadlowski A (2003) Review and comparative study of control techniques for three-phase PWM rectifiers. Math Comput Sim 63(3–5):349–361 Gil-lafuente AM, Merigo-lindahl JM (2010) Computational intelligence in business and economics-proceedings of the Ms’ 10 international conference world scientific Malinowski M (2001) Sensorless control strategies for three-phase PWM Rectifiers. PhD Thesis, Warsaw University of Technology, Poland Ziane H, Retif JM, Rekioua T (2008) Fixed-switching-frequency DTC control for PM synchronous machine with minimum torque ripples. Can J Electr Comput Eng 33(3/4):183–189 Malinowski M, Jasinski M, Kazmierkowski MP (2004) Simple direct power control of threephase PWM rectifier using space-vector modulation (DPC-SVM). IEEE Trans Ind Electron 51(2):447–454 Beltran B, Ahmed-Ali T, Benbouzid MEH (2007) Sliding mode power control of variable speed wind energy conversion systems. In: IEEE international electric machines & drives conference, 2007, IEMDC’07, vol 2 Mcgowan JG, Manwell JF (1999) Hybrid/PV/diesel system experiences. Rev Renew Energy 16:928–933 Rekioua T, Rekioua D, Laporte B, Benmahammed K (2000) Position sensor of torque ripple minimization of VSI fed AC machines. In: Conference record of the 2000 IEEE industry applications conference. Thirty-Fifth IAS annual meeting and world conference on industrial applications of electrical energy (Cat. No.00CH37129), vol 2. Rome, Italy, pp 1153–1158. https://doi.org/10.1109/IAS.2000.881977 El Khadimi A, Bachir L, Zeroual Et A (2004) Sizing optimization and techno-economic energy system hybrid photovoltaic-wind with storage system. Renewa Energy J 7:73–83

Chapter 8

Hybrid Systems in Wind Power

8.1 Introduction Wind-hybrid systems are energy systems that combine wind power with other energy sources or energy storage systems to meet the energy demands of a specific location or application. These systems are designed to provide a reliable and efficient power supply by harnessing the complementary characteristics of wind power and other energy sources.

8.2 Advantages and Disadvantages of a Hybrid System 8.2.1 Advantages of Hybrid System • Renewable Energy Generation: Hybrid wind systems harness clean and renewable wind power, reducing reliance on fossil fuels and contributing to environmental sustainability. • Cost Savings: By utilizing wind power and combining it with other energy sources, hybrid wind systems can reduce fuel consumption and operational costs, leading to potential long-term financial benefits. • Reduced Emissions: Hybrid wind systems minimize greenhouse gas emissions, helping to mitigate climate change and promote a cleaner environment. • Energy Security: By diversifying energy sources, hybrid wind systems reduce dependence on a single source and enhance overall energy security.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 D. Rekioua, Wind Power Electric Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-031-52883-5_8

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8 Hybrid Systems in Wind Power

• Increased Reliability: Integrating wind power with other energy sources improves the stability and reliability of the power supply, especially in variable wind conditions. • Off-Grid Applications: Hybrid wind systems provide sustainable and independent power solutions for remote or off-grid locations with limited grid access. • Grid Support: Hybrid wind systems can be connected to the grid, allowing for power supply to the grid during high wind generation and grid power utilization during low wind periods.

8.2.2 Disadvantages of a Hybrid System • System Complexity. • Designing and integrating multiple energy sources in a hybrid wind system requires careful planning and system optimization, increasing overall complexity. • Control and Management: Effective control strategies are necessary to balance and coordinate different energy sources within a hybrid wind system, requiring expertise and ongoing monitoring. • Maintenance and Operation: Regular maintenance and monitoring of wind turbines and other components are necessary for optimal performance, requiring specialized resources and expertise. • Space and Infrastructure: Hybrid wind systems require adequate space for wind turbines and associated infrastructure, including suitable land, storage facilities, and transmission infrastructure. • Initial Investment: Hybrid wind systems often require a significant upfront investment, including the cost of wind turbines, additional energy sources, and system integration, which can be a barrier to implementation.

8.3 Configuration of Hybrid Systems In a hybrid system, the generators can be connected in different configurations to meet specific requirements and optimize system performance [1, 2].

8.3 Configuration of Hybrid Systems

251

8.3.1 Architecture of DC Bus In the hybrid system presented in the following figure, the power supplied by each source is centralized on a DC bus. Thus, the energy conversion system to provide AC power at their first rectifier has to be converted then continuously. The generators are connected in series with the inverter to power the loads alternatives. The inverter should supply the alternating loads from the DC bus and must follow the set point for the amplitude and frequency [3]. The batteries are sized to supply peak loads. The advantage of this topology is the simplicity of operation and the load demand is satisfied without interruption even when the generators charge the short-term storage units (Fig. 8.1).

8.3.2 Architecture of AC Bus In this topology, all components of the HPS are related to alternating loads, as shown in Fig. 8.2. This configuration provides superior performance compared to the previous configuration, since each converter can be synchronized with the generator so that it can supply the load independently and simultaneously with other converters [2]. This provides flexibility for the energy sources which fed the load demand. In the case of low load demand, all generators and storage systems are stationary except for example the photovoltaic generator to cover the load demand. However, during heavy load demands or during peak hours, generators and storage units operate in parallel to cover the load demand. The realization of this system is relatively complicated because of parallel operation, by synchronizing the output voltages with the charge voltages [1]. This topology has several advantages compared to the DC coupled topology such as higher overall efficiency, smaller sizes of the power conditioning unit while keeping a high level of energy availability, and optimal operation of the diesel generator due to reducing its operating time and consequently its maintenance cost [4].

8.3.3 Architecture of DC/AC Bus The configuration of DC and AC bus is shown in Fig. 8.3. It has superior performance compared to the previous configurations. In this case, renewable energy and diesel generators can power a portion of the load directly to AC, which can increase system performance and reduce the power rating of the diesel generator and the inverter. The diesel generator and the inverter can operate independently or in parallel by synchronizing their output voltages. Converters located between two buses (the rectifier and inverter) can be replaced by a bidirectional converter, which in normal operation, performs the conversion DC/AC (inverter operation). When there is a surplus of

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8 Hybrid Systems in Wind Power

Fig. 8.1 Configuration of the hybrid system with DC bus

energy from the diesel generator, it can also charge batteries (operating as a rectifier). The bidirectional inverter can supply the peak load when the diesel generator is overloaded [5]. The advantages of this configuration are: • The diesel generator and the inverter can operate independently or in parallel. When the load level is low, one or the other can generate the necessary energy. However, both sources can operate in parallel during peak load.

8.3 Configuration of Hybrid Systems

253

Fig. 8.2 Configuration of the hybrid system with AC bus

• The possibility of reducing the nominal power of the diesel generator and the inverter without affecting the system’s ability to supply peak loads. The disadvantages of this configuration are: • The implementation of this system is relatively complicated because of the parallel operation (the inverter should be able to operate autonomously and operate with synchronization of the output voltages with the output voltages of the diesel generator).

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Fig. 8.3 Configuration of the hybrid system with AC bus and DC bus

8.3.4 Comparison of the Three Configurations Table 8.1 provides the three configurations of hybrid wind systems along with their descriptions, advantages, and drawbacks.

8.3.5 Classifications of Hybrid Energy Systems The power delivered by the hybrid system can vary from a few watts for domestic applications up to a few megawatts for systems used in the electrification of small islands [6]. Thus, for hybrid systems with a power below 100 kW, the configuration with AC and DC bus, with battery storage, is the most used. The storage system uses

8.3 Configuration of Hybrid Systems

255

Table 8.1 The three configurations of hybrid wind systems Configuration

Description

Advantages

Drawbacks

DC bus architecture

All generators are connected to a common DC bus

1. Simplified power management and control through DC voltage regulation

1. Limited compatibility with AC loads and existing AC infrastructure

2. Efficient utilization of 2. Challenges in DC sources without integrating with the the need for AC-DC grid due to DC nature conversion 3. Direct connection of DC loads to the DC bus, eliminating AC-DC conversion losses AC bus architecture

Each generator is connected to an individual AC bus

1. Flexibility in managing different AC sources and loads

1. Complex coordination and synchronization of multiple AC sources

2. Seamless integration 2. Potential power losses with existing AC due to AC-DC and infrastructure and grid DC-AC conversions connection 3. Ability to support a wide range of AC loads DC-AC bus architecture

Combination of DC and AC buses in the hybrid system

1. Efficient utilization of 1. Increased complexity both DC and AC and cost compared to sources individual DC or AC bus configurations 2. Flexibility in managing different types of loads, including both DC and AC loads

2. Potential power losses due to DC-AC and AC-DC conversions

3. Integration with both DC and AC infrastructure and grid connection options

a high number of batteries to be able to cover the average load for several days. This type of hybrid system uses small renewable energy sources connected to the DC bus. Another possibility is to convert the continuous power to an alternative one by using inverters. Hybrid systems used for applications with very low-power (below 5 kW) supply generally DC loads (Table 8.2).

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8 Hybrid Systems in Wind Power

Table 8.2 Classification of hybrid systems by power range Power of hybrid system [kW]

Applications

Low power: 500

Large isolated systems (Islands)

8.4 Different Combinations of Hybrid Systems 8.4.1 Hybrid Wind/Photovoltaic System The optimization of wind energy and photovoltaic with electrochemical storage (batteries) depends on many economic models of each system separately (wind and photovoltaic). The advantage of a hybrid system depends on many important factors: the shape and type of load, the wind, solar radiation, cost and availability of energy, the relative cost of the machine wind, solar array, electrochemical storage system, and other efficiency factors [7]. Photovoltaic systems are currently economical for low-power installations. For autonomous systems, the cost of energy storage is the biggest constraint on the overall system cost for large power installations. Minimizing the cost of storage and reducing its capacity are the main reason for the combination of wind and photovoltaic systems [8]. This type of hybrid system includes a photovoltaic subsystem (Fig. 8.4). A DC/DC parallel type can catch up whenever the maximum power point. A wind turbine which converts wind energy into electricity. Both energy sources are connected to a DC bus. Battery and inverter are included as part of the backup and storage system. The effectiveness of any electric system depends on its sizing and its use. The sizing should be based on meteorological data, solar radiation, wind speed, and the exact load profile of consumers over long periods.

Fig. 8.4 Hybrid wind/photovoltaic system

8.4 Different Combinations of Hybrid Systems

8.4.1.1

257

Determination of the Load Profile of Consumers

The exact knowledge of the customers load profile determines the size of the generator [9].

8.4.1.2

Analysis of Solar and Wind Energy Potential

We make application in Bejaia (Algeria) which is a coastal region with two complementary sources (wind speed and radiation), so the coupling of a photovoltaic system and wind is very interesting for the production of electricity throughout the year.

8.4.1.3

Photovoltaic Energy Calculation

The energy produced by a photovoltaic generator is estimated using data from the global irradiation on an inclined plane, ambient temperature, and the data sheet used for the photovoltaic panel. The electrical energy produced by a photovoltaic generator is given by E pv = η pv .A pv G

(8.1)

With Apv is the total area of the photovoltaic generator, ηgen is the efficiency of the photovoltaic generator.   η pv = ηr .η pc 1 − αsc (T j − T jr e f )

(8.2)

where G is a solar radiation on tilted plane module, ηr is the reference efficiency of the photovoltaic generator, η pc is the power conditioning efficiency which is equal to 1 if a perfect maximum power tracker (MPPT) is used, αsc is the temperature coefficient of short-current (A/°K) and found on the data sheet, T j cell temperature, T jr e f is the reference cell.

8.4.1.4

Wind Energy Calculation

The power contained in the form of kinetic energy, in the wind is expressed by Pwind =

1 3 .ρ.Swind .vwind 2

(8.3)

The energy produced by wind generator is expressed by E wind = Pwind .t

(8.4)

258

8.4.1.5

8 Hybrid Systems in Wind Power

Sizing of Photovoltaic and Wind Systems

The monthly energy produced by the system per unit of area is denoted Epv,m (kWh/ m2 ) for photovoltaic energy and Ewind,m (kWh/m2 ) for wind energy, and EL,m represents the energy required by load every month (where m = 1,2, …, 12 represents the month of the year). The total energy produced by both photovoltaic and wind a generator supplying the load is expressed by E L = E pv .A pv + E wnd .Swind

(8.5)

Where • For photovoltaic generator: A pv =

E L ,m E pv,m

(8.6)

E L ,m E wind,m

(8.7)

• For wind generator: Swind = With E pv .A pv = f pv .E L E wind .Swind = (1 − f pv ). E L = f wind .E L

(8.8)

where fpv is the fraction of load supplied by the photovoltaic energy • fwind = (1–fpv ) is the fraction of load supplied by the wind energy. • fpv = 1: indicates that the entire load is supplied by the photovoltaic source. • fpv = 0: indicates that the entire load is powered by the wind source. The sizing is based on the monthly annual average [10, 11]. The calculation of the size of wind generator and photovoltaic (Apv et Swind ) is established from the annual average values of each monthly contribution (E pv and E wind ). The load is represented by the annual average energy E L . A pv = f.

EL E pv

Swind = (1 − f ).

(8.9) EL E wind

8.4 Different Combinations of Hybrid Systems

259

The number of photovoltaic and wind generators to consider is calculated according to the area of the system unit taking the full value of the report by excess.  A pv N pv = E N T A pv,u   Swind Nwind = E N T Swind,u 

8.4.1.6

(8.10)

Sizing Batteries (See Chap. 1 Sect. 1.3.5)

..

8.4.1.7

Control of Hybrid Photovoltaic/wind System

Managing energy sources (photovoltaic and wind) is provided by a supervisor. For the design of the supervisor, it was decided that the sub-photovoltaic system would be the main generator, while the subsystem wind generator would be complementary. This choice is motivated by the design already made based on monthly averages annual rating site. However, the supervisor applications extend to considering the sub-wind system as the main generator and the photovoltaic subsystem would be complementary. Three operating modes are possible to determine the ability of the hybrid system to supply the total power required (the power load and the power required to charge the batteries) and those based on atmospheric conditions (insolation, temperature, and wind speed). This supervisor is essential to effectively control energy subsystems (photovoltaic and wind). We can have three cases (Tables 8.1 and 8.3) [9, 12]. In this case 1, the objective of the photovoltaic system is under power control according to this reference: Pr e f 1_P V = Pr equir ed = Vbatt .(Iload + Ibatt )

(8.11)

With: Iload is the load’s current, Ibatt is the battery’s current, Prequired is the total required power. In the cases 2 and 3, the PV system produces maximum power at MPPT operation. Different algorithms can be used to extract the maximum power (see Chap. 4). The reference power is given by opt opt opt Pr e f 2 _ P V = Ppv = Psopt = V pv .I pv

(8.12)

The wind system starts its operation when the PV power is insufficient to supply the total power required. The supervisor controls the wind system by power control

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8 Hybrid Systems in Wind Power

Table 8.3 The different cases Cases

Main generator Complementary generator

Operating mode

Case 1

Photovoltaic

Wind

The photovoltaic system is the main generator, and the wind system serves as a complementary source. The supervisor manages energy sources based on monthly average and annual ratings

Case 2

Wind

Photovoltaic

The wind system is the main generator, and the photovoltaic system acts as a complementary source. The supervisor adjusts the energy management strategy based on wind speed

Case 3 (hybrid operation)

Photovoltaic, Wind

Both the photovoltaic and wind systems contribute to power generation. The supervisor dynamically adjusts their contribution based on real-time atmospheric conditions and power demand

or by maximum power operation. The objective in case 2 is to produce the additional power to supply the total power applied. The wind power reference is given by Pr e f 1_w = Pr equir ed − Psopt = Vbatt .(Iload + Ibatt − Is )

(8.13)

When the contribution of wind power subsystem is no longer sufficient to supply the total power required the supervisors’ witches in the case 3. The objective of this subsystem is the generation of maximum power extraction. In this case 3, the wind system produces maximum power MPPT, the reference power is given by 3 Pr e f 2_w = Pwopt = K opt .ωopt

(8.14)

With Kopt is a coefficient which depends on the ratio of tip speed and optimal power coefficient. The reference angular velocity which corresponds to the operating MPPT is given by  ωr e f = ωopt =

3

Pr e f 2−w K opt

(8.15)

Then the supervisor decides the case (1 or 2/3) by comparing the measured mechanical speed with the reference speed.

8.4 Different Combinations of Hybrid Systems

261

Fig. 8.5 Description of operating cases

⎧ ⎨ ⎩

I f ω  ωopt , case1,

Pw = Pr e f 1_w (8.16)

I f ω = ωopt , case2/3,

opt

Pw = Pr e f 2_w = Pw

A description of operating cases is shown in Fig. 8.5.

8.4.2 Hybrid Wind/Photovoltaic/Diesel Generator System This type of hybrid system is well suited for decentralized production of electricity and can contribute to solving the problem of connecting to the electricity networks (cases of isolated sites) [5, 6]. The initial data in the implementation of such a system of production to renewable sources of energy like any other energy system is the demand, which will be determined by comparing the load to be supplied. This request must be estimated as accurately as possible both from a standpoint of powers called as its temporal distribution, even if its random nature makes this often difficult task. Adding a generator to a system of renewable energy production, may on the one hand increase the reliability of power system loads and reduce significantly the cost of electricity produced by a significant decrease of the size of the storage system [5, 7, 13]. Reference [10] proposed that there are multiple types of electrical circuit architectures which could be used depending on people’s needs and site capabilities. In the first architecture (Fig. 6.4), the generators and the battery are all installed in one place and are connected to a main AC bus bar before being connected to the grid. The power is delivered by all the energy conversion systems and the battery is fed to the grid through a single point. In this case, the power produced by the PV system and the battery is inserted into the AC before being connected to the main AC bus. This system is called centralized AC bus architecture (Fig. 8.6). The energy conversion systems can also be connected to the grid in another manner (Fig. 8.7). This system is called decentralized AC bus architecture. The power sources in this case do not need to be connected to one main bus bar. The power generated by each source is conditioned separately to be identical to that required by the grid. The third architecture uses a main centralized DC bus bar (Fig. 8.8). The energy

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8 Hybrid Systems in Wind Power

Fig. 8.6 Centralized AC bus architecture of hybrid wind/photovoltaic/diesel generator system

conversion systems produced by AC power (wind energy converter and the diesel generator) deliver their power to rectifiers to be converted into DC and then it is delivered to the main DC bus bar. A main inverter feeds the AC grid from this main DC bus. The monitoring equipment includes data loggers, wind speed and direction sensors, ambient and battery temperature sensors, and various AC and DC current/ voltage/power sensors. The purposes for using monitoring systems are [14]: • Determine components and system efficiencies. • Verify proper system functioning.

8.4 Different Combinations of Hybrid Systems

263

Fig. 8.7 Distributed AC bus architecture of hybrid wind/photovoltaic/diesel generator system

• Provide system trouble shooting. • Detect and analyze significant load changes. • Calculate actual cost of utilized energy. We propose a system Control of the hybrid wind/PV//Diesel system (Fig. 8.9) [15]. It is based on the overall energy balance equation. Pdiesel = Pload − Pwind − Ppv + Pdiss − Punm

(8.17)

where Pdiesel is the power delivered from the diesel generator(s), PLoad is the power required by the load, PWind is the power delivered from the wind turbine,  Pdiss is the power dissipated in the dump load, Punm is the unmet load, and Ppv is the power delivered from the PV. The power control unit (PCU) is a central location for making the various connections of subsystems (wind, photovoltaic, diesel generator). The monitoring system’s role is to manage and control the operation of a hybrid power system, depending on the weather (irradiance, wind speed) and the power required. The manager controls the opening and closing of three relays under the following conditions:

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8 Hybrid Systems in Wind Power

Fig. 8.8 Centralized DC bus architecture of hybrid wind/photovoltaic/diesel generator system

• The relay of the PV generator is open if: – Batteries are charged. – Current output by the PV generator is zero. – Load power is zero. • The relay wind generator is open if: – – – –

Batteries are charged. Wind speed is less than the initial speed of the turbine. Wind speed is greater than the stall speed of the wind. Load power is zero.

• The relay of the Diesel generator is open if: – Batteries are charged. – Generators (wind and PV) give a power greater than the load power. – Load power is zero and the batteries are charged.

8.4 Different Combinations of Hybrid Systems

265

Fig. 8.9 Control of hybrid wind/photovoltaic/diesel generator system

And the closure of this relay is when the state of the battery charger reaches the minimum level. From these conditions, we find that the monitoring system includes 06 inputs, 03 outputs, and 06 tests (Table 8.4). • The different tests are (Fig. 8.10): – – – – –

Test on the PV power Ppv = 0 or G = 0 (⇔A). Test on the wind speed (⇔ B). Test on the load power Pload = 0(⇔ C). Test on PV and wind power Ppv + Pwind ≥ Pload (⇔ D). Test on voltage battery Vbatt ≤ Vmin (⇔ E).

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8 Hybrid Systems in Wind Power

Table 8.4 Inputs and output variables Inputs variables

Outputs variables

Insolation (G)

Tpv the control signal relay of the PV generator

Wind speed (Vwind )

Twind the signal relay control of the wind generator

PV generator power o (Ppv )

Tdiesel the control signal relay of the diesel generator

Wind power generator (Pwind ) Power load (Pload ) Battery voltage (Vbatt )

Fig. 8.10 Supervisor of hybrid wind/photovoltaic/diesel generator system with battery storage

– Test on voltage battery Vbatt ≥ Vmax (⇔F). From the number of tests, we determine the number of possible combinations that we calculate during the following equation [16]: X c = 2ninp

(8.18)

where X is number of possible combinations and ninp is the number of inputs. We can obtain 64 combinations, but the number of possible combinations is reduced to 36. The logical equations are determined and give the control signals of the relays from each source. T pv = (E.F + E. F)(A.D + A.B.C.D + A.B.C.D.E.F) Twind = (E.F + E. F)(B.D + A.B.C.D + A.B. C.D.E.F)

(8.19)

Tdiesel = (E.F. D)(A.B + A.B.C + A.B.C) The bloc simulation under MATLAB/Simulink is represented in Fig. 8.11. Some results under different profiles of solar irradiance and wind speed variations are represented in Fig. 8.12.

8.4 Different Combinations of Hybrid Systems

267

Fig. 8.11 Bloc system of the hybrid PV/Wind/diesel generator system with battery storage

8.4.3 Hybrid Photovoltaic/Wind/Hydro System These systems consist of micro-hydro, solar, and wind plants [17] (Fig. 8.13) and can be combined by a diesel generator backup. The power control unit (PCU) is used to supervise and control the operations of PV/wind/hydro-diesel hybrid power system. It coordinates when power should be generated by PV panels, wind turbine, and hydro turbine and when it should be generated by diesel generator. The use of diesel generator is only when the demand cannot be sufficient by others energy.

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8 Hybrid Systems in Wind Power

Fig. 8.12 Simulation results of hybrid wind/photovoltaic/diesel generator system with battery storage

8.4 Different Combinations of Hybrid Systems

269

Fig. 8.13 Description of hybrid photovoltaic/wind/hydro system

8.4.4 Hybrid Photovoltaic/Wind/Fuel Cell System The necessary changes in our energy supply system can be accomplished if we use a hybrid system with solar, wind energies, and fuel cell. Generally, the overall system comprises a wind subsystem with an AC/DC rectifier to connect the wind generator to the DC bus. It also consists of a PV subsystem connected to the DC bus via a filter and DC/DC converter. The excess energy is stored as electrolytic hydrogen through an electrolyser and we use a fuel cell to generate electricity during low solar irradiations and low wind speeds (Fig. 8.14). In fact, when supply and demand do not coincide we need a convenient way to both store and transport renewable energy. This is where hydrogen comes into play as a storage and transport medium. When excess electric energy from wind and solar energy is stored in hydrogen and then converted back to electricity we have a solarwind hydrogen energy cycle. Wind and solar, fuel cells, and electrolysis use excess electricity to split water into oxygen and hydrogen. When we need electricity the gases are fed into a fuel cell which converts the chemical energy of the hydrogen (and oxygen) into electricity, water, and heat.

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8 Hybrid Systems in Wind Power

Fig. 8.14 Hybrid wind/PV/fuel cell configuration

8.5 Conclusion The study of hybrid wind systems presented in this chapter has provided valuable insights into their configurations, combinations, and applications. Various synoptic schemes and simulation applications have been described to showcase the potential of these systems. One specific study focused on a hybrid wind/PV/Diesel system with battery storage, which was simulated using MATLAB/Simulink. The study examined the system’s performance under different profiles of solar irradiance and wind speed, representing low, medium, and high conditions. The findings of this study are promising and highlight the potential of hybrid wind systems in various applications such as electrification and pumping systems. The combination of wind, PV, and Diesel sources along with battery storage offers an effective solution for meeting energy demands in remote or off-grid areas. The results obtained from the simulations provide valuable insights for system design, optimization, and operational strategies. In conclusion, the research conducted in this chapter contributes

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to the understanding and advancement of hybrid wind systems, demonstrating their applicability and effectiveness in real-world scenarios. Further research and development in this field can lead to more efficient and sustainable energy solutions in various sectors.

References 1. Wu Q, Sun Y (2018) Status of wind power technologies. In: Modeling and modern control of wind power. IEEE, pp 1−10.https://doi.org/10.1002/9781119236382.ch1 2. Xu D, Blaabjerg F, Chen W, Zhu N (2018) Basics of wind power generation system. In: Advanced control of doubly fed induction generator for wind power systems. IEEE, pp 21−42. https://doi.org/10.1002/9781119172093.ch2 3. Yaramasu V, Wu B (2017) Basics of wind energy conversion systems (Wecs). In: Model predictive control of wind energy conversion systems. IEEE, pp 1−60. https://doi.org/10.1002/978 1119082989.ch1 4. Padiyar R, Kulkarni AM (2019) Modeling and simulation of wind power generators. In: Dynamics and control of electric transmission and microgrids. IEEE, pp 115−143. https:// doi.org/10.1002/9781119173410.ch4 5. El-Okda Y, Adref K, Chikhalsouk M, Al Hajjar H (2019) Design of a small horizontal axis wind turbine. In: Advances in science and engineering technology international conferences (ASET). Dubai, United Arab Emirates, pp 1−7. https://doi.org/10.1109/ICASET.2019.8714319 6. Schubel PJ, Crossley RJ (2012) Wind turbine blade design. Energies 5:3425–3449. https://doi. org/10.3390/en5093425 7. Ojaghi M, Faiz J (2008) Extension to multiple coupled circuit modeling of induction machines to include variable degrees of saturation effects. IEEE Trans Magn 44(11):4053–4056. https:// doi.org/10.1109/TMAG.2008.2002405 8. Kishore A, Kumar GS (2006) A generalized state-space modeling of three phase self-excited induction generator for dynamic characteristics and analysis. In: Industrial electronics and applications, 1st IEEE Conference. pp 1–6. 9. Kishore A, Kumar GS (2006) Dynamic modelling and analysis of three phase self-excited induction generator using generalized state-space approach. In: International symposium on power electronics, electrical drives, automation and motion (SPEEDAM’06). IEEE, pp 52–59 10. Manwell JF, McGowan JG, Rogers AL (2010) Wind energy explained: theory, design and application, Willey Edition. 11. Malik et NH, Al-Bahrani AH (1990) Influence of the terminal capacitor on the performance characteristics of a self excited induction generator. IEEE Proc 137(2):168–173 12. Nejmi A, Zidani et Y, Naciri M (2002) Investigation on the self-excited induction generator provided with a hydraulic regulator, FIER, Tome II, Tétouane, Maroc. pp 494–499 13. Rekioua D, Rekioua T, Idjdarene et K, Tounzi A (2005) An approach for the modeling of an autonomous induction generator taking into account the saturation effect. Int J Emerg Electr Power Syst 4(1):1–25 14. Wang et L, Deng R (2006) A novel analysis of an autonomous three-phase delta-connected induction generator with one capacitor. In: Power engineering society general meeting. IEEE, pp 1–6 15. Elhafyani ML, Zouggar S, Benkaddour et M, Zidani Y (2006) Permant and dynamic behaviours of self-excited induction generator in balanced mode. Maroccan Stat Phys Soc 7(1):49–53 16. Wang et L, Kuo SC (2002) Steady state performance of a self-excited induction generator under unbalanced load, vol 1. In: Power engineering society winter meeting. IEEE, pp 408–412 17. Nesba A, Ibtiouen et R, Touhami O (2006) Dynamic performances of self-excited induction generator feeding different static loads. Serb J Electr Eng 3(1):63–76

Chapter 9

Examples and Importance of Wind Systems

9.1 Introduction Wind energy conversion systems (WECSs) generate electricity by harnessing the wind’s kinetic energy, providing a clean and sustainable energy source. This contributes to less dependency on fossil fuels, lower greenhouse gas emissions, and climate change mitigation. Furthermore, the development, installation, and maintenance of wind farms create jobs in manufacturing, construction, operations, and maintenance. This encourages economic progress while also benefiting local communities. Additionally, wind farms are usually built in rural areas, providing additional cash to landowners while also contributing to rural development. This is especially important in areas where there are limited economic possibilities. Finally, the deployment of WECSs has significant consequences that extend beyond energy generation, including environmental, social, and economic variables. Wind turbines are expected to play an even larger role in the global energy landscape as technology advances.

9.2 Some Examples of Wind Turbines 9.2.1 Wind Turbine of 600 W In our Laboratory LTII (university of Bejaia-Algeria), we have installed a 600 W wind turbine (Fig. 9.1). Its technical specifications are given in Table 9.1. Electrical specifications are summarized in Table 9.2. The performance and monthly energy output of 600 wind turbines are represented, respectively, in Figs. 9.2 and 9.3.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 D. Rekioua, Wind Power Electric Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-031-52883-5_9

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9 Examples and Importance of Wind Systems

Fig. 9.1 Example of a horizontal wind turbine of 600 W Table 9.1 Technical specifications of 600 W wind turbine

Table 9.2 Electrical specification 600 W wind turbine

Number of blades

02

Diameter

02 m

Material

Fiberglass and carbon fiber

Direction of rotation

Counterclockwise

Control systems

Electronic regulator

Alternator

Three-phase permanent magnet

Magnets

Ferrites

Nominal power

600 W

Nominal speed

1000 rpm

Regulator

12 V 60 A

275

Power (W)

9.2 Some Examples of Wind Turbines

Wind speed (m/s) Fig. 9.2 Performance of 600 wind turbine

Monthly energy output (kWh)

Energy

Average annual wind speed (m/s) Fig. 9.3 Monthly energy output of 600 wind turbine

9.2.1.1

Identification of the PMSM Parameters

The machine used is a permanent magnet synchronous machine with rotor smooth, with an output of 600 W (Table 9.3). To calculate the values of resistance and the stator inductance per phase we use the following scheme (Fig. 9.4). In order to avoid the short-circuit of the source, we inserted a resistance Rs between the source and a phase stator. Where Rs and L s are, respectively, the stator phase resistance and inductance, Rs is the inserted resistance.

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Table 9.3 Electrical parameters of 600 W PMSM

Value

Parameter

PN

600 W

Rs

0.457

Ls

0.029 H

τm

4.721 s

ϕf

0.13 Wb

P

17

J

0.1 kg.m2

f

0.06 N.m.s/rd

Three measurements of voltage and current were performed. We have Rins =

U I

(9.1)

Thus: Rins = 17.416  and then we calculate the stator resistance Rs = 0.457 . To determine the inductance, we have Z=

u i

where Z=



(2Rs + Rins )2 + 4L 2s .ω2

(9.2)

Thus  Ls =

(u/i)2 − (2Rs + Rins )2 4.ω2

(9.3)

We obtain L s = 0.029H . A wind speed of 17 m/s, the electrical speed ωe = 196.349 rad/s thus P = 17. The excitation flux is  f = 0.13 W b, the rotor inertia is J = 0.1 kg.m 2 , and the viscous coefficient is about f = 0.06 N .m.s/rad. The voltage at speed wind measured for Vwind = 17 m/s is shown in Fig. 9.5.

9.2.1.2

Simulations Results

We make an application of the WECS of 600 W under MATLAB/Simulink. We choose a profile wins speed (Fig. 9.6). The different simulation results are shown in Figs. 9.7, 9.8, 9.9, 9.10 and 9.11.

9.2 Some Examples of Wind Turbines Rins

277

A

Rs

V Rs

Ls

Fig. 9.4 Diagram of two-phase stator fed by a DC or AC source

Fig. 9.5 Voltage at speed wind (Vwind = 17 m/s)

We remark that the electrical power follows the wind speed profile with a maximum value of power which corresponds to a wind speed of 13 m/s.

9.2.1.3

Wind Turbine of 1 kW

The turbine comprises a permanent magnet brushless alternator (Fig. 9.12), which combined with Whisper’s high efficiency composite airfoil blade design, delivers

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9 Examples and Importance of Wind Systems

Fig. 9.6 Voltage at speed wind of Vwind = 17 m/s

Fig. 9.7 Power factor

9.2 Some Examples of Wind Turbines

Fig. 9.8 Current waveforms

Fig. 9.9 Voltage waveforms

279

280

9 Examples and Importance of Wind Systems

Fig. 9.10 Powe waveform

Fig. 9.11 Electromagnetic torque

900 watts of peak power at 28 mph (12.5 m/s). It is designed to operate with medium to high wind speed averages of 12 mph and greater (see Tables 9.4 and 9.5).

9.2 Some Examples of Wind Turbines Table 9.4 Technical specifications of wind turbine of 900 W

Table 9.5 PMSM electrical parameters of 900 W

281

Rotor diameter

2.1 m

Voltage

12 V, 24V DC

Start up wind speed

3.4 m/s

Survival wind speed

55 m/s

Over speed protection

Side furling

Rated power

900 W at 12.5 m/s

Turbine controller

Whisper controller

Kilowatt hours per month

100 KWh/month at 5.4 m/s

Values

Parameters

PN

900 W

Rs

0.49

Ls

0.0016 H

τm

4.721 s

ϕf

0.148 Wb

P

5

J

1.6

f

0.0001

Its applications cover stand-alone or hybrid systems, telecommunication, remote home, and small applications. The different results are represented in Figs. 9.13, 9.14 and 9.15. The overall installed system in our laboratory comprises a wind turbine, a whisper controller (Fig. 9.16) which offers greater reliability and superior control for battery charging, a battery bank (Fig. 9.17), fuses, and electrical protection cards (Fig. 9.18). For voltage sensors (Fig. 9.19), we use the Hall effect sensor LV25P equivalent to a transformer which is composed of a primary coil and a secondary coil. For the current sensor, its principle is the same as that of the voltage one, measuring current creates an output voltage proportional. It differs from the voltage sensor by the number of input pins and doesn’t require a resistor to limit the input current as the previous sensor (Fig. 9.20).

9.2.1.4

PMSM Parameters Identification

Some tests are important to confirm that the wind generator was not damaged in shipment and is ready to install on the tower. • Ground test. • Open circuit test.

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9 Examples and Importance of Wind Systems

Fig. 9.12 Example of a horizontal wind turbine of 900 W

• Short circuit test. • Phase-to-phase test. Different tests have been made (Figs. 9.21 and 9.22). The different simulation results are shown in Figs. 9.23, 9.24, 9.25 and 9.26.

9.2.1.5

Practical Results

An anemometer (Fig. 9.27) with wind sensor, temperature, and humidity sensor is arranged on the wind turbine with an Indoor Unit Display (Fig. 9.28). We present some results of signal measured at different frequencies (Figs. 9.29, 9.30, 9.31, 9.32 and 9.33).

283

Monthly energy output (kWh)

9.3 Importance of the Growing New Projects of WECSs in the World

Annual average Wind speed (m/s)

Power (W)

Fig. 9.13 Monthly energy output

Wind speed (m/s) Fig. 9.14 Output power

9.3 Importance of the Growing New Projects of WECSs in the World Wind turbine systems have been installed all around the world, marking a significant presence in the global energy landscape. Wind energy is a rapidly growing business, with new projects being constructed on a regular basis. Moreover, wind energy has experienced remarkable global growth, with an increasing number of countries investing in wind power projects. Furthermore, wind turbine technology, materials,

9 Examples and Importance of Wind Systems

Power coefficient

284

Speed Tip ratio Fig. 9.15 Power coefficient of wind power 900

Fig. 9.16 Installed hybrid wind-photovoltaic system

9.3 Importance of the Growing New Projects of WECSs in the World

285

Fig. 9.17 Battery bank

and design developments have improved efficiency and reduced costs, making wind energy more competitive with traditional fossil fuels. In addition to technological advancements, many governments have put regulations and incentives in place to encourage the development and installation of wind energy plants. Feed-in tariffs, tax credits, and renewable energy objectives are all part of this supportive framework. While onshore wind farms are still common, offshore wind projects have increased significantly. Located in bodies of water such as seas and oceans, offshore wind farms benefit from strong, persistent winds. Furthermore, wind turbines are becoming more efficient, and their capacity factors, which measure actual energy generation vs. maximum potential, are improving. Furthermore, wind power helps to decentralize energy production by allowing communities and regions to generate their own electricity and minimize their reliance on centralized power systems. Additionally, wind energy has produced jobs and stimulated economic growth in areas where wind farms have been established. Jobs in manufacturing, installation, operation, and maintenance contribute to the economic vibrancy of these regions. Moreover, wind energy is a clean and sustainable energy source that generates electricity without creating greenhouse gases or other pollutants associated with traditional fossil fuels. Wind power’s levelized cost of electricity (LCOE) has become increasingly competitive with traditional sources, making it an appealing alternative for utilities and investors. Finally, the financial world is increasingly interested in investing in renewable energy projects, such as wind, because they are viewed as long term, sustainable investments. Figure 9.34 shows some important points supporting the importance of the growing new projects and applications of wind turbine systems in the world.

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9 Examples and Importance of Wind Systems

Fig. 9.18 Whisper controller with cards protection and fuses

Fig. 9.19 Voltage sensor

9.3 Importance of the Growing New Projects of WECSs in the World

Fig. 9.20 Current sensor

Fig. 9.21 Wind turbine tests

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9 Examples and Importance of Wind Systems

Power coefficient

Fig. 9.22 PMSM of the wind turbine

Speed Tip ratio Fig. 9.23 Power coefficient

9.3 Importance of the Growing New Projects of WECSs in the World

Fig. 9.24 Voltage and current waveforms

Fig. 9.25 Speed waveform

289

290

Fig. 9.26 Braking test machine Fig. 9.27 Anemometer

9 Examples and Importance of Wind Systems

9.3 Importance of the Growing New Projects of WECSs in the World

Fig. 9.28 Indoor unit display of the anemometer

Fig. 9.29 Example of signal at f = 21.348 Hz

291

292

Fig. 9.30 Example of signal at f = 27.482 Hz

Fig. 9.31 Example of signal at f = 24.873 Hz

9 Examples and Importance of Wind Systems

9.3 Importance of the Growing New Projects of WECSs in the World

Fig. 9.32 Example of signal at f = 17.9969 Hz

Fig. 9.33 Example of signal at f = 21.106 Hz

293

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9 Examples and Importance of Wind Systems

Fig. 9.34 Main keys in importance of the growing new projects of WECSs in the world

9.4 Conclusion This chapter has provided two wind turbine examples that include both simulation and experimental tests. These examples are valuable resources for students pursuing degrees in electrical engineering, as well as those pursuing postgraduate studies. The presented examples offer a quick and comprehensive understanding of wind turbine identification and facilitate the simulation of the studied systems. By engaging with these examples, students can enhance their knowledge and practical skills in the field of wind energy. They can gain insights into the various aspects of wind turbine systems, including modeling, control, and performance analysis. The combination of simulation and experimental tests allows students to bridge the gap between theoretical concepts and real-world applications. The availability of these examples simplifies the learning process, enabling students to grasp complex topics related to wind turbines more easily. They can apply the knowledge gained from these examples to their own research projects, design considerations, and system optimizations. In conclusion, the inclusion of simulation and experimental tests in this chapter serves as a valuable educational resource for students, empowering them to develop a deeper understanding of wind turbine systems and fostering their practical skills in the field of electrical engineering.

Chapter 10

Power Management Control of Wind Energy Conversion Systems

10.1 Introduction Power management control (PMC) of wind energy conversion systems is a crucial aspect in ensuring efficient and reliable operation. It involves controlling the conversion of wind energy into electrical power while considering various factors such as wind speed, turbine performance, grid conditions, and energy storage. The primary goal of power management control is to maximize power output, improve system stability, and ensure optimal utilization of available resources [1–20]. PMC strategies in wind energy conversion systems typically include (Fig. 10.1): 1. Maximum Power Point Tracking (MPPT): MPPT algorithms are used to continuously adjust the turbine’s operating parameters to capture the maximum available power from the wind. These algorithms track the optimal operating point by dynamically adjusting parameters such as turbine speed, blade pitch angle, or generator torque [21]. 2. Pitch Control: Pitch control mechanisms adjust the angle of the turbine blades to regulate power output and maintain turbine performance within safe operating limits. It helps in controlling the aerodynamic forces acting on the rotor blades and ensures stable operation under varying wind conditions. 3. Generator Control: Generator control strategies regulate the output power of the wind turbine generator to match the grid requirements. This involves adjusting the generator’s electrical characteristics, such as voltage and frequency, to maintain grid stability and meet power demand [22]. 4. Energy Storage Management: Energy storage systems, such as batteries or supercapacitors, are often integrated into wind energy conversion systems to store excess energy during periods of low demand or high wind speeds. Power management control ensures efficient charging and discharging of the energy storage system to balance power supply and demand [23].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 D. Rekioua, Wind Power Electric Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-031-52883-5_10

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.1 PMC in WECS

5. Grid Integration Control: Wind energy conversion systems are typically connected to the electrical grid. Grid integration control strategies manage the interaction between the wind system and the grid, including synchronization, power quality control, and grid stability maintenance.

10.2 Advantages and Drawbacks The different advantages and drawbacks of PMC are summarized Fig. 10.2 [21–62].

Fig. 10.2 Advantages and drawbacks of PMC in WECS

10.3 Examples of Some PMC of Wind Systems

297

Fig. 10.3 PMC of wind battery system

10.3 Examples of Some PMC of Wind Systems 10.3.1 Wind/Battery System The power management control of a wind turbine/battery system involves optimizing the generation, storage, and utilization of electrical power to ensure efficient and reliable operation [35, 48] (Fig. 10.3). The key aspects and strategies involved in power management control are shown in Fig. 10.4. Figure 10.5 outlines the advantages and drawbacks of power management control (PMC) in a wind/battery system.

10.3.2 Wind/PV/Battery System Power management control of a wind/PV/battery system involves coordinating the operation of multiple energy sources, including wind turbines, photovoltaic (PV) panels, and energy storage batteries (Fig. 10.6). The primary objective is to optimize power generation, storage, and utilization to ensure reliable and efficient operation [2, 28, 29, 52, 63]. The key aspects of power management control for a wind/PV/battery system are (Fig. 10.7).

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.4 Key factors for PMC of wind turbine/battery system

Fig. 10.5 Advantages and drawbacks of power management control (PMC) in a wind/battery system

Figure 10.8 provides the advantages and drawbacks of power management control in a wind/PV/battery system.

10.3 Examples of Some PMC of Wind Systems

299

Fig. 10.6 Structure of wind/PV/battery system

Fig. 10.7 Key aspects of PMC for a wind/PV/battery system

Fig. 10.8 Advantages and drawbacks of power management control in a wind/PV/battery system

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10 Power Management Control of Wind Energy Conversion Systems

10.3.3 Wind/Diesel/Battery System Power management control in a wind/diesel/battery system involves the coordination and optimization of power generation, storage, and distribution to ensure efficient and reliable operation (Fig. 10.9). It aims to maximize the utilization of renewable energy sources, reduce dependence on diesel fuel, and maintain a stable power supply [8, 36]. The main components of the power management control system in a wind/diesel/ battery system are shown in (Fig. 10.10). 1. Wind Turbine Control: The control system for the wind turbine manages its operation based on wind speed and other environmental factors. It regulates the turbine’s output power and controls the pitch angle or rotor speed to optimize energy capture while avoiding over speeding or excessive loads.

Fig. 10.9 Structure of a wind/diesel generator/battery system

Fig. 10.10 Main components of PMCs in a wind/diesel generator/battery system

10.3 Examples of Some PMC of Wind Systems

301

Fig. 10.11 Advantages and drawbacks of PMC in a wind/diesel generator/battery system

2. Diesel Generator Control: The control system for the diesel generator monitors the power demand and activates the generator when the wind power and battery storage are insufficient to meet the load requirements. It ensures that the generator operates efficiently and maintains stable voltage and frequency. 3. Battery Energy Storage Control: The battery energy storage system (BESS) control manages the charging and discharging of the batteries. It determines the optimal charging and discharging rates based on the power demand, availability of wind power, and diesel generator status. The control algorithm considers battery state of charge (SOC), depth of discharge (DOD), and ensures optimal battery lifespan. 4. Power Distribution Control: The power management control system determines the allocation of power from different sources (wind, diesel, and battery) to meet the load demand. It constantly monitors the power supply and demand, adjusts the distribution of power, and maintains a stable grid voltage and frequency. 5. Energy Management System (EMS): The EMS serves as the central control unit that integrates and coordinates the operation of the wind turbine, diesel generator, battery storage, and power distribution control. It optimizes the power flow, manages the switching between different power sources, and ensures efficient utilization of available resources. Figure 10.11 gives the advantages and drawbacks of power management control in a wind/diesel/battery system.

10.3.4 Wind/Hydrogen/Battery System Power management control in a wind/hydrogen/battery system involves the efficient utilization and coordination of power generation from wind turbines, hydrogen

302

10 Power Management Control of Wind Energy Conversion Systems

production and storage, and battery energy storage. The control system ensures the optimal operation of these components to meet the power demand, maximize the use of renewable energy, and maintain system stability [32] (Fig. 10.12). The key aspects of power management control in a wind/hydrogen/battery system (Fig. 10.13). Figure 10.14 summarizes the advantages and drawbacks of power management control in a wind/hydrogen/battery system.

Fig. 10.12 Structure of wind/hydrogen/battery system

Fig. 10.13 Key aspects of PMC in a wind/hydrogen/battery system

10.3 Examples of Some PMC of Wind Systems

303

Fig. 10.14 Advantages and drawbacks of PMC in a wind/hydrogen/battery system

10.3.5 Wind Power Generation System with Compressed Air Energy Storage Power management control in a Wind power generation system with compressed air energy storage (CAES) involves the coordination and control of the wind turbines and the CAES system to ensure efficient and reliable operation. The control system aims to optimize the utilization of wind energy, manage the charging and discharging of the CAES system, and meet the power demand effectively [30, 36] (Fig. 10.15). The key aspects of power management control in a wind/CAES system are (Fig. 10.16). Figure 10.17 gives the advantages and drawbacks of power management control in a wind power generation system with compressed air energy storage.

10.3.6 Wind/Hydroelectric/Battery System Power management control in a wind/hydroelectric/battery system involves the coordination and control of the wind turbines, hydroelectric generators, and battery storage to optimize the utilization of renewable energy and ensure reliable power supply. The control system aims to balance the generation from wind and hydro sources, manage the charging and discharging of the battery, and meet the power demand efficiently (Fig. 10.18). The key aspects of power management control in a wind/hydroelectric/battery system are (Fig. 10.19).

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.15 Structure of a wind/CAES system

Fig. 10.16 Key aspects of PMC in a wind/CAES system

Figure 10.20 summarizes the advantages and drawbacks of power management control in a wind/hydroelectric/battery system.

10.3.7 Wind/Flywheel System Power management control in a wind/flywheel system involves regulating the flow of power between the wind turbine and the flywheel to ensure optimal operation and efficient energy storage. The control system aims to maximize the utilization of wind energy while maintaining stability and reliability, [24, 37, 64], (Fig. 10.21). The key aspects of power management control in a wind/flywheel system are (Fig. 10.22).

10.3 Examples of Some PMC of Wind Systems

305

Fig. 10.17 Advantages and drawbacks of PMC in a wind power generation system with CAES

Fig. 10.18 Structure of wind/hydroelectric/battery system

Figure 10.23 gives the advantages and drawbacks of power management control in a wind/flywheel system.

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.19 Key aspects of PMC in a wind/hydroelectric/battery system

Fig. 10.20 Advantages and drawbacks of PMC in a wind/hydroelectric/battery system

Fig. 10.21 Structure of wind/flywheel system

10.3 Examples of Some PMC of Wind Systems

307

Fig. 10.22 Key factor of PMC in wind/flywheel system

Fig. 10.23 Advantages and drawbacks of power management control in a wind/flywheel system

10.3.8 Wind/Supercapacitor Energy Storage Power management control in a wind/supercapacitor energy storage system involves regulating the flow of power between the wind turbine and the supercapacitor bank to maximize energy storage and utilization. The control system aims to efficiently capture and store excess energy from the wind turbine and release it during periods of high demand or low wind conditions [52] (Fig. 10.24).

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.24 Structure of wind turbine/supercapacitor energy storage system

Fig. 10.25 Key aspects of PMC in a wind/supercapacitor energy storage system

The key aspects of power management control in a wind/supercapacitor energy storage system are (Fig. 10.25). Figure 10.26 outlines the advantages and drawbacks of power management control in a wind/supercapacitor energy storage system.

10.3.9 WTb/Battery/Flywheel Power management control in a wind turbine (WT)/battery/flywheel hybrid system involves coordinating the operation of these components to optimize power generation, storage, and utilization. The control system aims to maximize the utilization of renewable wind energy while ensuring stability, reliability, and efficient energy storage (Fig. 8.26).

10.3 Examples of Some PMC of Wind Systems

309

Fig. 10.26 Advantages and drawbacks of PMC in a wind/supercapacitor energy storage system

Fig. 10.27 Structure of WTb/battery/flywheel hybrid system

The key aspects of power management control in a WTb/battery/flywheel hybrid system are (Fig. 10.28). Figure 10.29 gives the advantages and drawbacks of power management control in a WTb/battery/flywheel hybrid system.

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.28 Key aspects PMC in WTb/battery/flywheel hybrid system

Fig. 10.29 Advantages and drawbacks of PMC in a WTb/battery/flywheel hybrid system

10.3.10 WTb/Battery/Diesel Generator Power management control in a wind turbine (WTb)/battery/diesel generator hybrid system involves coordinating the operation of these components to optimize power generation, storage, and utilization. The control system aims to maximize the utilization of renewable wind energy while ensuring stability, reliability, and efficient energy management (Fig. 10.30). The key aspects of power management control in a WTb/battery/diesel generator hybrid system (Fig. 10.31).

10.3 Examples of Some PMC of Wind Systems

311

Fig. 10.30 Structure of WTb/battery/diesel generator hybrid system

Fig. 10.31 Key aspects of PMC in a wind turbine (WTb)/battery/diesel generator hybrid system

Figure 10.32 provides the advantages and drawbacks of power management control in a wind turbine (WTb)/battery/diesel generator hybrid system.

10.3.11 Wind Turbine/Battery/Supercapacities Power management control in a wind turbine/battery/supercapacitor hybrid system involves coordinating the operation of these components to optimize power generation, storage, and utilization. The control system aims to maximize the utilization of renewable wind energy while ensuring stability, reliability, and efficient energy management (Fig. 10.33). The key aspects of power management control in a WTb/battery/supercapacitor hybrid system (Fig. 10.34). Figure 10.35 shows the advantages and drawbacks of power management control in a wind turbine (WTb)/battery/supercapacitor hybrid system.

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10 Power Management Control of Wind Energy Conversion Systems

Fig. 10.32 Advantages and drawbacks of PMC in a WTb/battery/diesel generator hybrid system

Fig. 10.33 Structure of WTb/battery/supercapacitor hybrid system

10.3.12 WTb/Battery/Fuel Cells Power management control in a wind turbine (WT)/battery/fuel cell hybrid system involves coordinating the operation of these components to optimize power generation, storage, and utilization. The control system aims to maximize the utilization of renewable wind energy while ensuring stability, reliability, and efficient energy management (Fig. 10.36).

10.3 Examples of Some PMC of Wind Systems

313

Fig. 10.34 Keys aspects of PMC in WTb/battery/supercapacitor hybrid system ts

Fig. 10.35 Advantages and drawbacks of PMC in a WTb/battery/supercapacitor hybrid system

The key aspects of power management control in a WTb/battery/fuel cell hybrid system are (Fig. 10.37). Figure 10.38 gives the advantages and drawbacks of power management control in a wind turbine (WT)/battery/fuel cell hybrid system.

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Fig. 10.36 Structure of WTb/battery/fuel cell hybrid system

Fig. 10.37 Key aspects of PMC in a WTb/battery/fuel cell hybrid system

Fig. 10.38 Advantages and drawbacks of PMC in a WTb/battery/fuel cell hybrid system

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10.4 Conclusion In this chapter, power management control in wind energy conversion systems was presented. Different examples have been displayed with their advantages, drawbacks, and key factors. The choice of each configuration depends on key factors as grid connection requirements, control objectives, turbine Size and type, wind speed and variability, energy storage systems, cost considerations, and environmental and grid integration requirements such as noise limitations and bird protection regulations, as well as grid integration requirements, such as reactive power control or power factor correction. All these factors interact with each other, and the optimal PMC configuration for a specific WECS will depend on a careful evaluation of all these aspects.

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