DIAGNOSTIC MODEL OF INSULATION FAULTS IN POWER EQUIPMENT BASED ON ROUGH SET THEORY
Yan Zhang · Proceedings of the CSEE · 2004
Due to the incompleteness and complexity of fault diagnosis for power transformer, a specific fault diagnostic model with self-improvement method based on the Rough Set theory is given in this paper. After the statistic analysis on the collected fault examples of oil-immersed transformer, the results were reduced according to rough set theory and then the diagnostic rules were gotten also. In this paper, how to use this model for fault diagnosis under various conditions was described. Especially lacking key information, the reduced decision table may be gotten by synthetically matching the information came from artificial intelligences, i.e. Euclid Distance, Artificial Neural Network and Fuzzy Mathematics, and then the diagnosis might be completed by the reduced decision table and the accordingly rule set. Meanwhile the effectiveness of this model can be enhanced by the self-improvement method, which is achieved by modifying the decision table through richening training sample. The case studies show it is effective and useful.