Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids

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 h...

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Other Authors: Simões, Marcelo Godoy (Editor), Paredes, Helmo Kelis Morales (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
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Online Access:Get Fullteks
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245 1 0 |a Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids 
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506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a 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. 
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650 7 |a History of engineering & technology  |2 bicssc 
653 |a droop curve 
653 |a frequency regulation 
653 |a fuzzy logic 
653 |a the rate of change of frequency 
653 |a reserve power 
653 |a smart grid 
653 |a energy Internet 
653 |a convolutional neural network 
653 |a decision optimization 
653 |a deep reinforcement learning 
653 |a electric load forecasting 
653 |a non-dominated sorting genetic algorithm II 
653 |a multi-layer perceptron 
653 |a adaptive neuro-fuzzy inference system 
653 |a meta-heuristic algorithms 
653 |a automatic generation control 
653 |a fuzzy neural network control 
653 |a thermostatically controlled loads 
653 |a back propagation algorithm 
653 |a particle swarm optimization 
653 |a load disaggregation 
653 |a artificial intelligence 
653 |a cognitive meters 
653 |a machine learning 
653 |a state machine 
653 |a NILM 
653 |a non-technical losses 
653 |a semi-supervised learning 
653 |a knowledge embed 
653 |a deep learning 
653 |a distribution network equipment 
653 |a condition assessment 
653 |a multi information source 
653 |a fuzzy iteration 
653 |a current balancing algorithm 
653 |a level-shifted SPWM 
653 |a medium-voltage applications 
653 |a multilevel current source inverter 
653 |a motor drives 
653 |a phase-shifted carrier SPWM 
653 |a STATCOM 
653 |a electricity forecasting 
653 |a CNN-LSTM 
653 |a very short-term forecasting (VSTF) 
653 |a short-term forecasting (STF) 
653 |a medium-term forecasting (MTF) 
653 |a long-term forecasting (LTF) 
653 |a asynchronous motor 
653 |a linear active disturbance rejection control 
653 |a error differentiation 
653 |a vector control 
653 |a renewable energy 
653 |a solar power plant 
653 |a Data Envelopment Analysis (DEA) 
653 |a Fuzzy Analytical Network Process (FANP) 
653 |a Fuzzy Theory 
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