Dynamic state estimation including anomaly detection and identification for power systems

Abstract

A novel method for detecting and identifying three fundamental anomalies, i.e. occurrence of bad data, changes in network configuration and sudden variation of states, in dynamic state estimation for electric power systems is proposed. Examination of innovation processes in the Kalman filter is appropriately used in each step of the detection of anomalies, discrimination between anomalies and application of counterplans. Discrimination between the anomalies is performed by a test based on skewness or the chi-square value of distributions of the innovation processes, although changes in network configuration and sudden variations of states are not always separable. Counterplans, which are different for each case of the anomalies, are developed so as to prevent deterioration in the accuracy of the estimator. Simulation results show that this algorithm works excellently.