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Full Description
The urgent need to reduce carbon emissions is leading to growing use of renewable electricity, particularly from wind and photovoltaics. However, the intermittent nature of these power sources presents challenges to power systems, which need to ensure high and consistent power quality. Going forward, power systems also need to be able to respond to changes in loads, for example from EV charging. Neither production nor load changes can be predicted precisely, and so there is a degree of uncertainty or fuzziness. One way to meet these challenges is to use a kind of artificial intelligence - fuzzy logic.
Fuzzy logic uses variables that may be any real number between 0 and 1, rather than either 0 or 1. It has obvious advantages when used for optimization of alternative and renewable energy systems. The parametric fuzzy algorithm is inherently adaptive because the coefficients can be altered to accommodate requirements and data availability.
This book focuses on the use of fuzzy logic and neural networks to control power grids and adapt them to changing requirements. Chapters cover fuzzy inference, fuzzy logic-based control, feedback and feedforward neural networks, competitive and associate neural networks, and applications of fuzzy logic, deep learning and big data in power electronics and systems.
Contents
Chapter 1: Introduction
Chapter 2: Real-time simulation applications for future power systems and smart grids
Chapter 3: Fuzzy sets
Chapter 4: Fuzzy inference: rule based and relational approaches
Chapter 5: Fuzzy-logic-based control
Chapter 6: Feedforward neural networks
Chapter 7: Feedback, competitive, and associative neural networks
Chapter 8: Applications of fuzzy logic and neural networks in power electronics and power systems
Chapter 9: Deep learning and big data applications in electrical power systems
