Substation event analysis using information from intelligent electronic devices

The data acquisition capability of processor-based relays and intelligent electronic devices (IEDs) can improve reliability and reduce the global cost of the power system. Nevertheless, the quantity and complexity of the captured data is beyond the requirements of most utilities, particularly when ones consider their immediate operational needs. Though the data acquisition process has been highly automated, the process of assimilating and analysing 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 132/11 kV 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.