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dc.contributor.editorSimões, Marcelo Godoy
dc.contributor.editorParedes, Helmo Kelis Morales
dc.date.accessioned2021-05-01T15:43:54Z
dc.date.available2021-05-01T15:43:54Z
dc.date.issued2020
dc.identifierONIX_20210501_9783039433346_959
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/69213
dc.description.abstractArtificial 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.otherdroop curve
dc.subject.otherfrequency regulation
dc.subject.otherfuzzy logic
dc.subject.otherthe rate of change of frequency
dc.subject.otherreserve power
dc.subject.othersmart grid
dc.subject.otherenergy Internet
dc.subject.otherconvolutional neural network
dc.subject.otherdecision optimization
dc.subject.otherdeep reinforcement learning
dc.subject.otherelectric load forecasting
dc.subject.othernon-dominated sorting genetic algorithm II
dc.subject.othermulti-layer perceptron
dc.subject.otheradaptive neuro-fuzzy inference system
dc.subject.othermeta-heuristic algorithms
dc.subject.otherautomatic generation control
dc.subject.otherfuzzy neural network control
dc.subject.otherthermostatically controlled loads
dc.subject.otherback propagation algorithm
dc.subject.otherparticle swarm optimization
dc.subject.otherload disaggregation
dc.subject.otherartificial intelligence
dc.subject.othercognitive meters
dc.subject.othermachine learning
dc.subject.otherstate machine
dc.subject.otherNILM
dc.subject.othernon-technical losses
dc.subject.othersemi-supervised learning
dc.subject.otherknowledge embed
dc.subject.otherdeep learning
dc.subject.otherdistribution network equipment
dc.subject.othercondition assessment
dc.subject.othermulti information source
dc.subject.otherfuzzy iteration
dc.subject.othercurrent balancing algorithm
dc.subject.otherlevel-shifted SPWM
dc.subject.othermedium-voltage applications
dc.subject.othermultilevel current source inverter
dc.subject.othermotor drives
dc.subject.otherphase-shifted carrier SPWM
dc.subject.otherSTATCOM
dc.subject.otherelectricity forecasting
dc.subject.otherCNN–LSTM
dc.subject.othervery short-term forecasting (VSTF)
dc.subject.othershort-term forecasting (STF)
dc.subject.othermedium-term forecasting (MTF)
dc.subject.otherlong-term forecasting (LTF)
dc.subject.otherasynchronous motor
dc.subject.otherlinear active disturbance rejection control
dc.subject.othererror differentiation
dc.subject.othervector control
dc.subject.otherrenewable energy
dc.subject.othersolar power plant
dc.subject.otherData Envelopment Analysis (DEA)
dc.subject.otherFuzzy Analytical Network Process (FANP)
dc.subject.otherFuzzy Theory
dc.titleApplied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids
dc.typebook
oapen.identifier.doi10.3390/books978-3-03943-335-3
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783039433346
oapen.relation.isbn9783039433353
oapen.pages202
oapen.place.publicationBasel, Switzerland


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