Reprint
Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids
Edited by
October 2020
203 pages
- ISBN978-3-03943-334-6 (Hardback)
- ISBN978-3-03943-335-3 (PDF)
This book is a reprint of the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
Artificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids.
Format
- Hardback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
droop curve; frequency regulation; fuzzy logic; the rate of change of frequency; reserve power; smart grid; energy Internet; convolutional neural network; decision optimization; deep reinforcement learning; electric load forecasting; non-dominated sorting genetic algorithm II; multi-layer perceptron; adaptive neuro-fuzzy inference system; meta-heuristic algorithms; automatic generation control; fuzzy neural network control; thermostatically controlled loads; back propagation algorithm; particle swarm optimization; load disaggregation; artificial intelligence; cognitive meters; machine learning; state machine; NILM; non-technical losses; smart grid; semi-supervised learning; knowledge embed; deep learning; distribution network equipment; condition assessment; multi information source; fuzzy iteration; current balancing algorithm; level-shifted SPWM; medium-voltage applications; multilevel current source inverter; motor drives; phase-shifted carrier SPWM; STATCOM; smart grid; electricity forecasting; CNN–LSTM; very short-term forecasting (VSTF); short-term forecasting (STF); medium-term forecasting (MTF); long-term forecasting (LTF); asynchronous motor; linear active disturbance rejection control; error differentiation; vector control; renewable energy; solar power plant; Data Envelopment Analysis (DEA); Fuzzy Analytical Network Process (FANP); Fuzzy Theory