Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids
Simões, Marcelo Godoy
Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (202 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-335-3 9783039433346 9783039433353
10.3390/books978-3-03943-335-3 doi
History of engineering & technology
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 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 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
Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (202 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-335-3 9783039433346 9783039433353
10.3390/books978-3-03943-335-3 doi
History of engineering & technology
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 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 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