Title :
Static VAr compensator with neural network control
Author :
Mumyakmaz, B. ; Jin, Xianhc ; Wang, Changchang ; Cheng, T.C.
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
Artificial neural networks (ANNs) have been used in many applications of pattern classification, speech synthesis and recognition, function approximation, associative memory and control. Because of their adaptive nature and parallel computational features, they are promising a lot of hope for future of engineering. In this study, an application of ANN in control has been presented. A static VAr compensator model that has been used to provide reactive energy for a given load was controlled using two multi-layer feed-forward neural networks. ANN control method used in this study was more speedy than classic feedback control system. Because of their adaptivity and generalization features, ANNs provided power factor error less than 2% while inputs contained harmonics in this study. Results show that ANNs could be used to control power electronic circuits in static VAr compensators
Keywords :
control system analysis computing; control system synthesis; feedforward neural nets; multilayer perceptrons; neurocontrollers; power system analysis computing; power system control; reactive power control; static VAr compensators; adaptivity; artificial neural networks; generalization; neural network control; power factor error; reactive energy; static VAr compensator; two multilayer feedforward neural networks; Artificial neural networks; Associative memory; Concurrent computing; Function approximation; Neural networks; Pattern classification; Pattern recognition; Speech recognition; Speech synthesis; Static VAr compensators;
Conference_Titel :
Transmission and Distribution Conference, 1999 IEEE
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-5515-6
DOI :
10.1109/TDC.1999.756110
