| Description |
1 online resource : illustrations |
| Series |
IET energy engineering ; 91 |
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IET energy engineering series ; 91.
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| Contents |
Machine generated contents note: 1. Introduction -- 1.1. Introduction -- 1.2. Fuzziness -- 1.3. Fuzzy membership functions -- 1.4. Fuzzy sets -- References -- 2. Fuzzy sets -- 2.1. Introduction -- 2.2. Fuzzy sets and fuzzy membership functions -- 2.2.1. Triangular membership function -- 2.2.2. Trapezoid membership function -- 2.2.3. Gaussian membership function -- 2.2.4. Bell membership function -- 2.2.5. Cauchy membership function -- 2.2.6. Sinusoid membership function -- 2.2.7. Sigmoid membership function -- 2.3. Properties of fuzzy membership functions -- 2.4. Fuzzy set operations -- 2.4.1. Intersection: t-norm -- 2.4.2. Union: t-conorm -- 2.4.3.Complement -- 2.4.4. De Morgan laws -- 2.5. Adjustment of fuzziness -- 2.6. Problems -- References -- 3. Fuzzy partitioning -- 3.1. Introduction -- 3.2. Theoretical approaches -- 3.3. Fuzzy partition examples in energy systems -- 3.4. Problems -- References -- 4. Fuzzy relation -- 4.1. Introduction -- 4.2. Fuzzy relation -- 4.3. Operation with fuzzy relations |
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Note continued: 4.3.1. Intersection of two fuzzy relations -- 4.3.2. Union of two fuzzy relations -- 4.3.3. Negation of a fuzzy relation -- 4.3.4. Inverse of a fuzzy relation -- 4.3.5.Composition of fuzzy relations -- 4.3.6.Compositional rule of inference -- 4.3.7. The relational joint -- 4.4. Binary relations -- 4.5. The extension principle -- 4.5.1. The cylindrical extension -- 4.6. Fuzzy mapping -- 4.7. Problems -- References -- 5. Fuzzy reasoning and fuzzy decision-making -- 5.1. Introduction -- 5.2. Fuzzy implications -- 5.3. Approximate reasoning -- 5.4. Inference rules of approximate reasoning -- 5.4.1. Entailment rule of inference -- 5.4.2. Conjunction rule of inference -- 5.4.3. Disjunction rule of inference -- 5.4.4. Negation rule of inference -- 5.4.5. Projection rule of inference -- 5.4.6. Generalized modus ponens rule of inference -- 5.4.7.Compositional rule of inference -- 5.5. Fuzzy reasoning -- 5.5.1. Inference engine with single input single rule |
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Note continued: 5.5.2. Inference engine with multiple input single rule -- 5.5.3. Inference engine with multiple input multiple rule -- 5.6. Problems -- References -- 6. Fuzzy processor -- 6.1. Introduction -- 6.2. Mamdani fuzzy reasoning -- 6.2.1. Fuzzification -- 6.2.2. Fuzzy rule base -- 6.2.3. Fuzzy conclusion -- 6.2.4. Defuzzification -- 6.3. Takagi-Sugeno fuzzy reasoning -- 6.4. Tsukamoto fuzzy reasoning -- 6.5. Problems -- References -- 7. Fuzzy logic controller -- 7.1. Introduction -- 7.2. Physical system behaviors and control -- 7.3. Fuzzy processor for control -- 7.3.1. Fuzzy rules: the modeling of thoughts -- 7.3.2. The input -- output interaction -- 7.4. Modeling the FLC in MATLAB -- 7.5. Modeling the FLC in Simulink -- 7.6. Problems -- References -- 8. System modeling and control -- 8.1. Introduction -- 8.2. System modeling -- 8.3. Modeling electrical systems -- 8.4. Modeling mechanical systems -- 8.4.1. Mechanical systems with linear motion |
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Note continued: 8.4.2. Mechanical systems with rotational motion -- 8.5. Modeling electromechanical systems -- 8.5.1. Field subsystem -- 8.5.2. Armature subsystem -- 8.5.3. Mechanical subsystem -- 8.5.4. Electromechanic interaction subsystem -- 8.5.5. Modeling DC motors -- 8.5.6. Modeling AC motors -- 8.6. Problems -- References -- 9. FLC in power systems -- 9.1. Introduction -- 9.2. Excitation control -- 9.2.1. Excitation system modeling -- 9.2.2. State-space model of excitation systems -- 9.2.3. FLC of excitation systems -- 9.3. LF control -- 9.3.1. Small signal modeling of power systems -- 9.3.2. FLC design for LFC -- 9.4. FLC in power compensation -- 9.4.1. Power factor improvement -- 9.4.2. Bus voltage control -- 9.5. Problems -- References -- 10. FLC in wind energy systems -- 10.1. Introduction -- 10.2. Wind turbine -- 10.3. Electrical generator -- 10.3.1. Dynamic modeling of induction generator -- 10.3.2. Self-excited induction generator -- 10.4. FLC examples in WEC systems |
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Note continued: 10.5. Problems -- References -- 11. FLC in PV solar energy systems -- 11.1. Introduction -- 11.2. PV cell modelings -- 11.2.1. Reference I -- V characteristics of a PV panel -- 11.2.2. Effects of changes in solar irradiation and temperature -- 11.2.3. PV panel modeling in Simulink -- 11.2.4.A PV array emulator -- 11.3. MPP search in PV arrays -- 11.3.1. MPP by lookup tables -- 11.3.2. MPP search algorithm based on measurements of SX and TX -- 11.3.3. MPP search algorithm based on voltage and current measurements -- 11.3.4. MPP search algorithm based on online repetitive method -- 11.4. MPPT of PV arrays -- 11.4.1. Constant maximum power angle approach -- 11.4.2. Online load matching approach -- 11.5. Problems -- References -- 12. Energy management and fuzzy decision-making -- 12.1. Introduction -- 12.2. Distributed generation and control -- 12.3. Energy management in a renewable integration system -- 12.3.1. Centralized control of distributed renewable energy systems |
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Note continued: 12.3.2. Distributed control of renewable energy systems -- 12.4. Problems -- References |
| Summary |
This 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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| SUBJECT |
MATLAB. http://id.loc.gov/authorities/names/n92036881
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MATLAB fast |
| Subject |
Electric power systems -- Control.
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Fuzzy logic.
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Numerical analysis -- Computer programs.
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Fuzzy logic -- Congresses
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TECHNOLOGY & ENGINEERING -- Mechanical.
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Numerical analysis -- Computer programs
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Electric power systems -- Control
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Fuzzy logic
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fuzzy control.
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power control.
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| Genre/Form |
Conference papers and proceedings
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| Form |
Electronic book
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| ISBN |
9781785611087 |
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1785611089 |
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9781523112876 |
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1523112875 |
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