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, Xiao
Xu, Dehong
Leung, Frank H. F.
Wang, Yousheng
Lee, Yim-shu
Subjects: 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.
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.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1359
ISBN: 0-7803-5735-3
Appears in Collections:EIE Conference Papers & Presentations

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