Multiple bad data identification in power system state estimation using particle swarm optimization

This paper presents the approach of multiple bad data identification using particle swarm optimization (PSO). The identification problem is formulated as minimizing the number of bad data while maintaining the system observability and satisfying the chi²-test. The problem is considered as a combinatorial problem which could be solved by discrete binary PSO. The proposed method is tested on the IEEE 14-bus and IEEE 30-bus systems, where it could successfully identify multiple bad data with interacting and conforming errors. The proposed algorithm is also applied to an actual network of MEA, Thailand.