Intelligent data analysis for power systems

This paper presents potential techniques for automated analysis of various types of data collected in power systems. Diverse recording devices have been widely deployed in modern power systems, and as a result more data have been obtained. It is necessary to extract useful and actionable information from the captured data. This paper focuses on discussing intelligent techniques for automatically analyzing such data, including disturbance classification, fault events correlation, fault type classification, fault cause identification, fault location, generator monitoring and parameter estimation, incipient fault detection, and line parameter estimation.