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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorSun, X-
dc.creatorXu, D-
dc.creatorLeung, FHF-
dc.creatorWang, Y-
dc.creatorLee, YS-
dc.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.en_US
dc.rightsThis 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.en_US
dc.subjectFeedback controlen_US
dc.subjectIntelligent controlen_US
dc.subjectLearning systemsen_US
dc.subjectNeural networksen_US
dc.subjectTwo term control systemsen_US
dc.subjectUninterruptible power systemsen_US
dc.titleDesign and implementation of a neural-network-controlled UPS inverteren_US
dc.typeConference Paperen_US
dcterms.abstractA 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIECON'99 proceedings : the 25th annual conference of the IEEE Industrial Electronics Society : November 29-December 3, 1999, San Jose, California, USA, p. 779-784-
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