Rough Set-Based Data Analysis in Converter Fault Diagnosis for Hybrid Power Substation

Abstract

The fault in power system cannot be completely avoided. In this paper, we developed a method to resolve fault localization problems in power system. Though the data acquisition process has been highly automated, the process of assimilating and analyzing data still lags behind. Raw data must be transformed into knowledge in order to help users decide how to respond to the event and implement the necessary actions. A promising technique for substation event analysis using rough set theory is described in this paper. It interprets the data and outputs meaningful and concise information, which improves the performance of a data analysis system and help with the knowledge acquisition process. A substation model was developed to generate various fault scenarios for our case studies to evaluate the performance of the rough set algorithm. The results show that it works well and efficiently with the overwhelming data.