Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1359
Title: Design and implementation of a neural-network-controlled UPS inverter
Authors: Sun, X
Xu, D
Leung, FHF 
Wang, Y
Lee, YS
Keywords: Feedback control
Intelligent control
Learning systems
Neural networks
Two term control systems
Uninterruptible power systems
Issue Date: 1999
Publisher: IEEE
Source: IECON'99 proceedings : the 25th annual conference of the IEEE Industrial Electronics Society : November 29-December 3, 1999, San Jose, California, USA, p. 779-784 How to cite?
Abstract: A low-cost analog neural network control scheme for the inverters of Uninterruptible Power Supplies (UPS) is proposed to achieve low total harmonics distortion (THD) output voltage and good dynamic response. Such a scheme is based on learning control law from representative example patterns obtained from two simulation models. One is a multiple-feedback-loop controller for linear loads, and the other is a novel idealized load-current-feedback controller specially designed for nonlinear loads. Example patterns for various loading conditions are used in the off-line training of a selected neural network. When the training is completed, the neural network is used to control the UPS inverter on-line. A simple analog hardware is built to implement the proposed neural network controller, an optimized PI controller is built as well. Experimental results show that the proposed neural-network-controlled inverter achieves lower THD and better dynamic responses than the PI-controlled inverter does.
URI: http://hdl.handle.net/10397/1359
ISBN: 0-7803-5735-3
Rights: © 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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