Fuzzy Logic Control in Energy Systems with design applications in MATLAB®/Simulink®
Modern electrical power systems are facing complex challenges, arising from distributed generation and intermittent renewable energy. Fuzzy logic is one approach to meeting this challenge and providing reliability and power quality. The book is about fuzzy logic control and its applications in managing, controlling and operating electrical energy systems. It provides a comprehensive overview of fuzzy logic concepts and techniques required for designing fuzzy logic controllers, and then discusses several applications to control and management in energy systems. The book incorporates a novel fuzzy logic controller design approach in both Matlab® and in Matlab Simulink& so that the user can study every step of the fuzzy logic processor, with the ability to modify the code. Fuzzy Logic Control in Energy Systems is an important read for researchers and practicing engineers in energy engineering and control, as well as advanced students involved with power system research and operation.
Inspec keywords: fuzzy control; power control
Other keywords: fuzzy sets; energy systems; fuzzy partitioning; fuzzy decision maker; fuzzy processor; FLC; fuzzy logic controller; MATLAB; fuzzy decision making processing; fuzzy relation; Simulink
Subjects: Formal logic; Artificial intelligence (theory); Control logic; Power and energy control; Fuzzy control
- Book DOI: 10.1049/PBPO091E
- Chapter DOI: 10.1049/PBPO091E
- ISBN: 9781785611070
- e-ISBN: 9781785611087
- Page count: 520
- Format: PDF
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Front Matter
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1 Introduction
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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 introduced.
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2 Fuzzy sets
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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.
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3 Fuzzy partitioning
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Fuzzy subclasses and partitioning of the universes into fuzzy subsets are studied in this chapter.The importance and meaning of the portioning are discussed with examples.
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4 Fuzzy relation
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The concept of fuzzy relation, two-dimensional fuzzy sets, fuzzy extension principle, fuzzy projection, binary and n-ary fuzzy relations is discussed in this chapter. Representing verbal terms and expressions as fuzzy relations are also introduced in this chapter.
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5 Fuzzy reasoning and fuzzy decision-making
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Approximate reasoning, 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 reasoning is studied and user-defined MATLAB® files are used to support the operational behaviors of fuzzy decision-making.
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6 Fuzzy processor
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Fuzzy reasoning and fuzzy decision-making processes are carried out with multiple input, multiple rules and multiple decisions as the fuzzy processor. Known fuzzy reasoning algorithms such as Mamdani fuzzy reasoning, Sugeno fuzzy reasoning and Tsukamoto fuzzy reasoning are discussed and steps toward fuzzy logic controllers are given.
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7 Fuzzy logic controller
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Fuzzy logic controller (FLC) is given in this chapter. Rule development, 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.
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8 System modeling and control
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Mathematical modeling of physical systems is given in this chapter. The methods for 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.
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9 FLC in power systems
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Application of FLC and decision maker to excitation control, load-frequency control and power compensation are discussed in this chapter. Singleand multiarea control of power systems are also studied as examples in the chapter.
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10 FLC in wind energy systems
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Application of FLC and decision-making processes to wind energy conversion systems is given in this chapter. After providing the problems and control issues in wind energy conversion systems, the utilization of fuzzy logic in solving these problems is shown.
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11 FLC in PV solar energy systems
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Application of FLC and decision-making processes in PV solar systems is given in this chapter. Maximum power point tracking, sun tracking, voltage control, battery charging and management of the generated power are studied.
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12 Energy management and fuzzy decision-making
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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.
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Back Matter
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