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Fuzzy logic control in energy systems : with design applications in MATLAB /Simulink /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
The Institution of Engineering and Technology,
2017.
|
| Colección: | IET energy engineering series ;
91. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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
- Note continued: 12.3.2. Distributed control of renewable energy systems
- 12.4. Problems
- References.