Data Analytics Approach for Topology Change Detection and Identification in Power Systems

An undetected topology change caused by overgrown trees is the root cause for the 2003 large scale blackout. The discrepancy between the actual topology of the power grid and the graph model used in state estimation is considered a topology error. Topology error was a reason that the Weighted Least Square (WLS)-based state estimation failed to converge and report alarms. This presentation will cover non-WLS approaches for topology error detection. In particular, when the power system matrix is known, we develop a hypothesis testing approach based on the asymptotic distribution of the maxima; when the system matrix is unknown, we develop a realtime anomaly detection method based on time series change point detection, and a line outage identification method based on learning from PMU data. The line outage identification process is not tightly coupled with power flow analysis and state estimation, as these tasks require detailed and accurate information about the system matrices, but can achieve a high detection rate comparable to the previous work that involves solving power flow and state estimation equations. Both single line outage and multiple simultaneous line outages are considered.