• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.541-551
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• 1996

This study develops the real time ULTC(Under Load Tap Changer) control strategy with LDC setting values moved dynamically using artificial neural networks. The suggested strategy can improve the ULTC voltage compensation capability by building 2 types of neural networks, ANNs and ANNg. ANNs recognizes the uncompensated MTr sending voltage change caused by the receiving voltage variation. And ANNg dynamically determines the most appropriate ULTC setting valtage chanbe caused by the receiving voltage variation. And ANNg dynamically determines the most appropriate ULTC setting values by recognizing the voltage level obtained from ANNs, and the section load pattern for each time period. In order to evaluate the suggested approach, the ULTC voltage compensation strategy are simulated on a 8 feeder distribution system. Artificial neural networks developed in this study are implemented in FORTRAN language on PC 386.