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Volumn 3, Issue , 2007, Pages 1436-1439
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Control method for power quality compensation based on Levenberg-Marquardt optimized BP neural networks
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Author keywords
Harmonics compensation; Neural network; UPQC; Voltage sag
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Indexed keywords
ACTIVE FILTERS;
ARTIFICIAL INTELLIGENCE;
BACKPROPAGATION;
BACKPROPAGATION ALGORITHMS;
CELLULAR RADIO SYSTEMS;
COMPUTER NETWORKS;
CONVERGENCE OF NUMERICAL METHODS;
ELECTRIC MEASURING INSTRUMENTS;
ELECTRIC POTENTIAL;
ELECTRIC POWER FACTOR;
FUNCTION EVALUATION;
INTEGER PROGRAMMING;
LEARNING ALGORITHMS;
MATHEMATICAL MODELS;
MATLAB;
METROPOLITAN AREA NETWORKS;
MOTION CONTROL;
MOTION PLANNING;
NETWORK PROTOCOLS;
NEURAL NETWORKS;
OPTIMIZATION;
POWER ELECTRONICS;
POWER QUALITY;
VEGETATION;
ARTIFICIAL NEURAL NETWORKS (ANN);
BACK PROPAGATION NEURAL NETWORK (BPANN);
BP NEURAL NETWORKS (BPNN);
CLOSE COUPLING (CC);
COMPLEX OBJECTS;
CONFERENCE PROCEEDINGS;
CONTROL METHODS;
CONTROL SIGNALLING;
CURRENT COMPENSATION;
EFFICIENT LEARNING;
FASTER CONVERGENCE;
HARMONIC CURRENTS;
INTERNATIONAL (CO);
LEVENBERG-MARQUARDT;
LEVENBERG-MARQUARDT (LM) ALGORITHMS;
MULTIPLE OBJECTIVES;
MULTIPLE-INPUT , MULTIPLE-OUTPUT (MIMO);
NERVE CELLS;
POWER FACTOR (PF);
QUALITY COMPENSATION;
SIMULATION EXPERIMENTS;
SIMULATION MODELLING;
THREE PHASE;
TRADITIONAL CONTROL;
UNIFIED POWER QUALITY CONDITIONER (UPQC);
VOLTAGE SAGS;
VOLTAGE SUPPLY;
WEIGHTS TRAINING;
QUALITY CONTROL;
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EID: 45149116976
PISSN: None
EISSN: None
Source Type: Conference Proceeding
DOI: 10.1109/IPEMC.2006.297312 Document Type: Conference Paper |
Times cited : (11)
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References (9)
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