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|Title:||Neural-network-controlled single-phase UPS inverters with improved transient response and adaptability to various loads|
Uninterruptible power systems
|Source:||PEDS'99 : proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems : 27-29 July 1999, Hong Kong, p. 865-870 How to cite?|
|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.|
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|Appears in Collections:||Conference Paper|
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