Fuzzy Logic Control in Energy Systems: With design examples in MATLAB®/Simulink®

İsmail Hakkı Altaş

doi:10.1049/PBPO254E

Fuzzy Logic Control in Energy Systems: With design examples in MATLAB^®/Simulink^® (2nd Edition)

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Author(s): İsmail Hakkı Altaş ¹

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Publication Year: 2024

Fuzzy models have the capability of recognising, representing, and working with data that is vague or lacks certainty, making them suitable for managing electrical energy systems involving intermittent distributed generation and varying distributed loads.

The second edition of Fuzzy Logic Control in Energy Systems has been revised and updated with new topics including shade and integration issues in PV, microgrids, cybersecurity in power systems, and day-ahead estimation of sun and wind data. Starting with a review of fuzzy logic, chapters cover fuzzy sets and partitioning, fuzzy decision making, fuzzy logic controllers (FLC), and system modelling and control. The application of FLC in power systems, wind energy, PV energy systems and microgrids is then explored.

Several examples of fuzzy logic control and management are discussed including DC motor control, multi area load-frequency control, and wind and PV energy management. The book presents a fuzzy logic controller design in Matlab-Simulink enabling the reader to study every step and to develop their own fuzzy processor library and toolbox.

Providing thorough and logical coverage of fuzzy logic control and its use in energy systems, this book provides valuable insights for researchers and practicing engineers involved with power system design and control, grid operators, PV and wind turbine manufacturers, as well as advanced students in EE.

Inspec keywords: fuzzy set theory; knowledge based systems; uncertainty handling; distributed power generation; decision making; inference mechanisms; mathematics computing; fuzzy control; fuzzy reasoning

Other keywords: energy systems; decision making; fuzzy logic control; distributed power generation; fuzzy reasoning; knowledge based systems; inference mechanisms; mathematics computing; uncertainty handling; fuzzy set theory

Subjects: Reasoning and inference techniques; Specific control systems; Control of power systems and devices; Combinatorial mathematics; Combinatorial mathematics; Knowledge based systems; General electrical engineering topics; Power transmission, distribution and supply; General and management topics; Mathematics computing

Book DOI: 10.1049/PBPO254E
Chapter DOI: 10.1049/PBPO254E
ISBN: 9781839538827
e-ISBN: 9781839538834
Page count: 597
Format: PDF

Front Matter
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- p. (1)
1 Introduction
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- p. 1 –7 (7)
  
  A brief history of fuzzy set theory and its application areas are summarized in this chapter. Besides, an introductory knowledge of electrical energy systems and novel approaches to the problems arising with the developments of distributed generation and microgrids is given.
2 Fuzzy sets and fuzzy partitioning
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- p. 9 –85 (77)
  
  The concept of fuzziness, types, and properties of fuzzy sets, modeling of fuzzy sets in MATLAB^® and MATLAB/Simulink^® are shown and MATLAB function files are developed to be used as a part of a user-defined toolbox library. Fuzzy intersection, union, and complement have also been studied in this chapter. Besides, fuzzy subclasses and partitioning of the universes into fuzzy subsets are also studied in this chapter.
3 Fuzzy decision-making
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- p. 87 –155 (69)
  
  General information on fuzzy relation and the compositional rule of inference have been given in this chapter. Then, approximate reasoning, fuzzy reasoning, and fuzzy decision-making processes are given. Single input, multiple input, and multiple rule base systems are studied and examples are given. The concept of fuzzy reasoning and fuzzy decision makers is studied and user-defined MATLAB^® files are used to support the operational behaviors of fuzzy decision-making. Well-known fuzzy reasoning algorithms such as Mamdani fuzzy reasoning, Sugeno fuzzy reasoning, and Tsukamoto fuzzy reasoning have also been studied here.
4 Fuzzy logic controller (FLC)
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- p. 157 –242 (86)
  
  Fuzzy logic controller (FLC) theory and design approaches are given in this chapter. Machines operate the human way as if run by expert operators and steps toward FLCs are given. Rule development, the way of putting expert ideas into rules, and inference system structure are studied. From crisp input variables to crisp output, all process is discussed and shown. Defuzzification, rule processing, fuzzy reasoning, and crisp output after defuzzification are explained. Using the FLC as an optimization tool is introduced as a new subsection for this edition. User-developed FLC examples are given.
5 System modeling and control
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- p. 243 –310 (68)
  
  This chapter starts with mathematical modeling of physical systems. The methods of 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 fuzzy logic. The reader will be able to develop his/her own FLC code in MATLAB and in MATLAB/Simulink^®. Examples of controlling electrical, mechanical, and electromechanical systems are given.
6 FLC in power systems
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- p. 311 –372 (62)
  
  Application of fuzzy logic controller and decision maker to excitation control, load-frequency control, and power compensation are discussed in this chapter. Single- and multi-area control of power systems are also studied as examples in the chapter besides FL-based load flow analysis.
7 FLC in wind energy systems
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- p. 373 –410 (38)
  
  After a general introduction about wind energy systems, the application of fuzzy logic control and decision-making processes to wind energy conversion systems and their interfacing to utility are given in this chapter. Problems and control issues in wind energy conversion systems are studied and the utilization of fuzzy logic in solving these problems are shown.
8 FLC in PV solar energy systems
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- p. 411 –477 (67)
  
  Application of fuzzy logic control and decision-making processes in photovoltaic (PV) solar systems are given in this chapter. Modeling and characteristic properties of PV solar arrays are viewed in terms of maximum power point tracking (MPPT), sun tracking, voltage control, battery charging, interfacing issues, and management of the generated power. As new to this chapter, shaded effects of PV systems will be added and simulation examples will be given related to MPPT control under shading effects.
9 Microgrids and energy management
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- p. 479 –554 (76)
  
  This chapter has been re-written and re-organized. Subsections about distributed generation, distributed networks, and microgrids have been added to this chapter. Day-ahead forecasting of power to be generated from sun and wind will be added and combined with energy management. Both generation and demand side management and control issues are considered including cybersecurity problems and solution approaches. The fuzzy decision-making and controllers developed in previous chapters will be used in energy management systems in this chapter.
Back Matter
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Fuzzy Logic Control in Energy Systems: With design examples in MATLAB^®/Simulink^® (2nd Edition)

Buy e-book PDF

Front Matter

1 Introduction

2 Fuzzy sets and fuzzy partitioning

3 Fuzzy decision-making

4 Fuzzy logic controller (FLC)

5 System modeling and control

6 FLC in power systems

7 FLC in wind energy systems

8 FLC in PV solar energy systems

9 Microgrids and energy management

Back Matter

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Fuzzy Logic Control in Energy Systems: With design examples in MATLAB®/Simulink® (2nd Edition)

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Fuzzy Logic Control in Energy Systems: With design examples in MATLAB^®/Simulink^® (2nd Edition)