An intelligent power factor corrector for power system using artificial neural networks

作     者：Bayindir, R. Sagiroglu, S. Colak, I. 

作者机构：Gazi Univ Fac Tech Educ Dept Elect Educ TR-06500 Ankara Turkey Gazi Univ Fac Engn & Architecture Dept Comp Engn TR-06570 Ankara Turkey 

出 版 物：《ELECTRIC POWER SYSTEMS RESEARCH》 (电力系统研究)

年 卷 期：2009年第79卷第1期

页      面：152-160页

核心收录：

学科分类：0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 

主　　题：Artificial neural network Learning algorithm Power factor correction Synchronous motor Microcontroller 

摘      要：An intelligent power factor correction approach based on artificial neural networks (ANN) is introduced. Four learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. The best test results obtained from the ANN compensators trained with the four learning algorithms were first achieved. The parameters belonging to each neural compensator obtained from an off-line training were then inserted into a microcontroller for on-line usage. The results have shown that the selected intelligent compensators developed in this work might overcome the problems occured in the literature providing accurate, simple and low-cost solution for compensation. (C) 2008 Elsevier B.V. All rights reserved.