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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1417

Title: Neural-network-controlled single-phase UPS inverters with improved transient response and adaptability to various loads
Authors: Sun, Xiao
Xu, Dehong
Leung, Frank H. F.
Wang, Yousheng
Lee, Yim-shu
Subjects: Computer simulation
Electric distortion
Electric loads
Feedback control
Harmonic generation
Mathematical models
Neural networks
Transients
Uninterruptible power systems
Issue Date: 1999
Publisher: IEEE
Citation: PEDS'99 : proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems : 27-29 July 1999, Hong Kong, p. 865-870.
Abstract: This paper proposes a neural-network control scheme for the inverters of Uninterruptible Power Supplies (UPS) to improve their transient response and adaptability to various loads. Two simulation models are built to obtain example patterns for training the neural network. One is a multiple-feedback-loop controller for linear loads, and the other is an idealized load-current-feedback controller specially designed for nonlinear loads. The latter controller has a built-in reference load model, and the load current is forced to track this reference. Example patterns under various loading conditions are used in the off-line training of a selected neural network, which is made as simple as possible to reduce the calculation time. When the training is completed, the neural network is used to control the UPS inverter on-line. The development of example patterns and training of the neural network are done using MATLAB and SIMULINK, and the verification of results is done using PSpice. It is found that the proposed neural-network-controlled inverter can provide good sinusoidal output voltage with low Total Harmonic Distortion (THD) under various loading conditions, and good transient responses when the load changes.
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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1417
ISBN: 0-7803-5769-8
Appears in Collections:EIE Conference Papers & Presentations

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