Distributed Kalman-Like Filtering and Bad Data Detection in the Large-Scale Power System

This article investigates the distributed state estimation problem for large-scale power systems with the appearance of bad data. The power system is decomposed into several nonoverlapping agents and these agents interact with each other through transmission lines to form an interconnected multiagent power system (IMAPS). The measurement at each agent is local measurement, and the measurement in transmission line is edge measurement. To obtain an accurate state estimation of each agent in a distributed manner when the measurements are coupled with bad data, a bad data detection process should be designed. The difficulty is how to detect the bad data in edge measurement in a distributed scheme. To solve this problem, the characteristics of the edge measurement residual is analyzed, and a distributed bad data detection strategy is presented based on a novel iterative distributed Kalman-like filter (IDKF). It is proved that the IDKF algorithm can converge in finite steps when the communication graph of the IMAPS is acyclic, and the estimation accuracy is similar to that of the centralized Kalman filter. In addition, the IDKF algorithm shows excellent performance even when bad data appears. Simulation tests conducted on the IEEE 118-bus power system verify the theoretical findings.