Fuzzy Logic Control in Energy Systems in MatLab_Simulink(2017)

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基于MATLAB_SIMULINK的能源系统模糊控制 This book is about fuzzy logic controller (FLC) and its applications in energy systems. It aims to give an insight into a clear understanding and design approaches of FLCs in MATLAB and MATLAB/Simulink environment
Contents Preface Acknowledgments XV 1 Introduction 1.2 Fuzziness 1.3 Fuzzy membership functions 1. 4 Fuzzy sets References 2 Fuzzy sets 2.2 Fuzzy sets and fuzzy membership functions 13 2. 2.1 Triangular membership function 13 2.2.2 Trapezoid membership function 17 2. 2.3 Gaussian membership function 22 2.2.4 Bell membership function 23 2.2.5 Cauchy membership function 24 2.2.6 Sinusoid membership function 26 2. 2.7 Sigmoid membership function 2.3 Properties of fuzzy membership functions 36 2. 4 Fuzzy set operations 43 2.4.1 Intersection:t-norm 2.4.2 Union: t-conorm 46 2.4.3 Complement 48 2.4.4 De Morgan laws 5 2.5 Adjustment of fuzziness 53 2.6 Problems 55 References 60 3 Fuzzy partitioning 63 3.1 Introduction 3.2 Theoretical approaches 66 3.3 Fuzzy partition examples in energy systems 67 3.4 Problems 83 References 87 4 Fuzzy relation 89 4.1 Introduction 89 4.2 Fuzzy relation 89 4.3 Operation with fuzzy relations 4.3. 1 Intersection of two fuzzy relations 4.3.2 Union of two fuzzy relations 99 4.3.3 Negation of a fuzzy relation 4.3. 4 Inverse of a fuzzy relation 102 4.3.5 Composition of fuzzy relations 4.3.6 Compositional rule of inference 108 4.3.7 The relational joint 110 4. 4 Binary relations 110 4.5 The extension principle 4.5.1 The cylindrical extension 113 4.6 Fuzzy mapping 117 4.7 Problems 122 References 126 5 Fuzzy reasoning and fuzzy decision-making 127 5.1 Introduction 127 5.2 Fuzzy implications 127 5.3 Approximate reasoning 134 5.4 Inference rules of approximate reasoning 136 5.4.1 Entailment rule of inference 137 5. 4.2 Conjunction rule of inference 137 5.4.3 Disjunction rule of inference 137 5.4.4 Negation rule of inference 138 5.4.5 Projection rule of inference 138 5.4.6 Generalized modus ponens rule of inferenc 139 5.4.7 Compositional rule of inference 139 5.5 Fuzzy reasoning 140 5.5.1 Inference engine with single input single rule 142 5.5.2 Inference engine with multiple input single rule 143 5.5.3 Inference engine with multiple input multiple rule 146 5.6 Problems 156 References 158 6 Fuzzy processor 161 6.1 Introduction 161 6.2 Mamdani fuzzy reasoning 161 6.2. 1 Fuzzification 166 6.2.2 Fuzzy rule base 168 6.2.3 Fuzzy conclusion 168 6.2. 4 Defuzzification 171 6.3 Takagi-Sugeno fuzzy reasoning 178 6.4 Tsukamoto fuzzy reasoning 185 6.5 Problems 189 References 196 7 Fuzzy logic controller 199 7. 1 Introduction 199 7.2 Physical system behaviors and control 200 7.3 Fuzzy processor for control 210 7.3.1 Fuzzy rules: the modeling of thoughts 211 7.3.2 The input-output interaction 218 7.4 Modeling the FlC in MATLAB 222 7.5 Modeling the FlC in Simulink 231 7.6 Problems 244 References 248 8 System modeling and contr 251 8.1 Introduction 251 8.2 System modeling 252 8.3 Modeling electrical systems 259 8.4 Modeling mechanical systems 271 8.4.1 Mechanical systems with linear motion 272 8.4.2 Mechanical systems with rotational motion 279 8.5 Modeling electromechanical systems 282 8.5.1 Field subsystem 286 8.5.2 Armature subsystem 287 8.5.3 Mechanical subsystem 287 8.5.4 Electromechanic interaction subsystem 288 8.5.5 Modeling DC motors 290 8.5.6 Modeling AC motors 301 8.6 Problems 301 References 307 9 FLC in power systems 309 9.1 Introduction 309 9.2 Excitation contro 312 9. 2. 1 Excitation system modeling 315 9.2.2 State-space model of excitation systems 321 9.2. 3 FlC of excitation systems 323 9.3 LF control 328 9.3.1 Small signal modeling of power systems 329 9.3.2 FlC design for LFC 335 9. 4 FLC in power compensation 347 9.4.1 Power factor improvement 348 9.4.2 Bus voltage control 351 9.5 Problems 356 References 359 10 FLC in wind energy systems 363 10.1 Introduction 363 10.2 Wind turbine 364 10.3 Electrical generator 368 10.3.1 Dynamic modeling of induction generator 370 10.3.2 Self-excited induction generator 375 10.4 FLC examples in WeC systems 380 10.5 Problems 395 References 398 11 FLC in PV solar energy systems 403 11.1 Introduction 403 11.2 PV cell modelings 406 11.2. 1 Reference I-V characteristics of a PV panel 410 1. 2.2 Effects of changes in solar irradiation and emperature 413 11. 2.3 PV panel modeling in Simulink 418 11.2. 4 A PV array emulator 426 11. 3 MPP search in PV arrays 429 11.3.1 mpp by lookup tables 430 11. 3.2 MPP search algorithm based on measurements of Sand t 431 11.3.3 MPP search algorithm based on voltage and current measurements 432 1.3.4 MPP search algorithm based on online repetitive method 434 11.4 MPPT of Pv arrays 435 11.4.1 Constant maximum power angle approach 436 11.4.2 Online load matching approach 44 11.5 Problems 453 References 456 12 Energy management and fuzzy decision-making 459 12.1 Introduction 459 12.2 Distributed generation and control 461 12.3.1 Centralized control of distributed renewabl m 12.3 Energy management in a renewable integration syste 463 energy systems 463 12.3.2 Distributed control of renewable energy systems 484 12. 4 Problems 490 References 492 Index 495 Preface This book is about fuzzy logic controller(FLC) and its applications in energy systems. It aims to give an insight into a clear understanding and design approaches of flcs in matlab and matlab/simulink environment It includes a basic theory of fuzzy sets and fl to prepare the reader for a better understanding of fuzzy partitioning, fuzzy relation and fuzzy decision-making processing, which are required for designing FLCs. a fuzzy unit called fuzzy processor is developed and designed to be used as a fuzzy decision maker and a flc depending on the application problem Energy system is one of the application areas of FL. It is used to manage control and operate electrical energy systems. Examples in the book are related to the control, operation and management of electrical energy utilization. The fol- lowing examples on FLC and fuzzy management are discussed and studied in the scope of the book DC motor speed and torque control excitation and load-frequency control in power systems multiarea load-frequency control in power systems wind energy control systemS (WECS) photovoltaic(Pv) solar energy control systems maximum power point tracking in PV systems energy management in WECS energy management in PV systems The book addresses undergraduate and graduate students as well as practicing engineers in electrical power, energy and control systems. They will be able to get sufficient knowledge of Fl theory and a clear understanding of designing fuzzy decision maker and controller in MATLAB and Simulink Those who study the book will be able to develop their own fuzzy processor library and design their own fL toolbox for the special problems they study. With the given examples, the readers will also get to know the modeling and simulation of electrical power and energy systems A novel FlC design approach in both MATLaB and Simulink is given in the book such that the user can see every step of the fl processor with the ability to interfere the code in MATLAB m files and also in operational Simulink blocks The FlC design approach will make the readers not just as software users but also software developers. Chapters 1-7 can be used as an accompanying textbook for teaching Fuzzy Logic and Fuzzy Decision Making as an undergraduate course. Chapters 1-8 can be used as a textbook for teaching Fuzzy Logic Control in undergraduate or graduate levels. Chapters 5-9 can be used in a graduate course about flc in Power System Control assuming that students have a basic knowledge of fuzzy set theory and fl Chapters 5-7 and 10-12 can be used as an advanced graduate course about FlC in Renewable energy and Distributed Generation. Chapters 10-12 can also be used in an advanced graduate course to teach FL-controlled wind and Pv energy conver Sion systems. The book is organized into 12 chapters Chapter 1. Introduction. a brief history of fuzzy set theory and its application areas are summarized in this chapter The concept of fuzziness, fuzzy membershi functions and fuzzy subsets is introduced Chapter 2. Fuzzy sets. Types and properties of fuzzy sets are studied Modeling of fuzzy sets in MATLAB and matlab/Simulink are shown and MatlaB function files are developed to be used as a part of user-defined toolbox library Fuzzy intersection, union and complement are also studied in this chapter Chapter 3. Fuzzy partitioning Fuzzy subclasses and partitioning of the uni verses into fuzzy subsets are studied in this chapter. The importance of and meaning of the portioning are discussed with examples Chapter 4. Fuzzy relation. The concept of fuzzy relation, two-dimensional fuzzy sets, fuzzy extension principle, fuzzy projection and binary and n-ary fuzzy relations are discussed in this chapter. Representing verbal terms and expressions as fuzzy relations are also introduced in this chapter Chapter 5. Fuzzy reasoning and fuzzy decision-making. Approximate reason ing, fuzzy reasoning and fuzzy decision-making processes are given in this chapter. Single-input single-rule, single-input multiple-rules and multiple-input multiple rule base systems are studied and examples are given. The concept of fuzzy rea- soning is studied and user-defined matlab files are used to support the opera- tional behaviors of fuzzy decision-making Chapter 6. Fuzzy processor. Fuzzy reasoning and fuzzy decision-making processes are carried ahead with multiple inputs, multiple rules and multiple decisions as the fuzzy processor. Known fuzzy reasoning algorithms such as Mamdani fuzzy reasoning, Sugeno fuzzy reasoning and Tsukamoto fuzzy reason- ing are discussed and steps toward flCs are given Chapter 7. Fuzzy logic controller. FLC is given in this chapter. Rule devel opment, the way of putting experts'ideas into rules and inference system structure are studied. From crisp input variables to crisp output, all processes are discussed and shown. Defuzzification, rule processing, fuzzy reasoning and crisp output after defuzzification are explained. User-developed FLC examples are given Chapter 8 System modeling and control. Mathematical modeling of physical systems is given in this chapter. The methods obtaining differential equations simulation diagrams and state-space models of physical systems are studied Runge-Kutta numerical solution method is discussed and user-based matlaB software is developed to show the meaning of controlling physical systems as one of the application areas of FL. The reader will be able to develop his/her own FLC code in MATLAB and MATLAB Simulink. Examples of controlling electrical mechanical and electromechanical systems will be given Chapter 9. FLC in power systems. Application of FLC and decision maker to excitation control, load-frequency control and power compensation is discussed in this chapter. Single and multiarea control of power systems are also studied as examples in the chapter Chapter 10. FLC in wind energy systemS. Application of FL control and decision-making processes to wind energy conversion systems is given in this chapter. After giving problems and control issues in wind energy conversion sys tems, the utilization of fl in solving these problems is shown Chapter 11. Flc in Py solar energy systems. Application of Fl control and decision-making processes in PV solar systems is given in this chapter. Maximum power point tracking, sun tracking, voltage control, battery charging and manage- ment of the generated power are studied Chapter 12. Energy management and fuzzy decision-making. The use of fuzzy decision-making and control process in energy management systems is studied in this chapter. Energy management in PV solar and wind energy systems is discussed and examples are given Chapter I Introduction a brief history of fuzzy set theory and its application areas are summarized in this chapter. The concept of fuzziness, fuzzy membership functions and fuzzy subsets is ntroduced 1.1 Introduction Many words we use arbitrarily in our daily life are usually fuzzy in terms of verba meanings. When expressing or describing a system or an event, we use words such as old, young, tall, short, cold, warm, hot, sunny, cloudy, fast, slow, etc, which are fuzzy in nature. We, the humans, use uncertain, vogue and muddy words when discussing something or taking decisions to perform some actions. Depending on his/her age, we call a person old, middle aged, young, very old and very young. We press the gas or brake more or less according to the road condition whether it is dry slippery, ramp or flat. If the lights in our study room are low, we increase brightness a little, else we decrease it. all these examples show how our brain acts and takes decisions during the situations that are uncertain and fuzzy Studies on systems with uncertainty and muddy data have reached a new era with the publication of the article"Fuzzy sets"by lotfi A. Zadeh [1]. although this article was first published in 1965, the use of fuzzy logic(FL)has increased after the second half of the 1970s when lotfi A. Zadeh published two more articles [2,3], in which the application of fuzzy set theory to uncertain systems and decision-making was described. FL applications have been gaining a high speed ever since the Japanese started using them in commercially available appliances Nowadays, it is possible to find fuzzy-based applications in almost every area [4 Some of the utilization areas of fl are listed next FL is used in robotics, automation, tracking systems, temperature control, flow control, motion control, commercial products and many more utilization areas of automatic control systems [4-7]. It is used in information systems as a database tool to store and recall knowledge, uncertain data, experts'ideas and operational behavior of machines. Image processing, signal aliasing and human-machine interaction are also some of the application areas where FL is used [4]. It is possible to find many more FL-based applications in social and medical sciences as well [4]. FL is also used as a mathematical tool in areas such as function optimi- zation, filtering, curve fitting, etc. [4]

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