Multiple Bad Data Identification Using Binary Particle Swarm Optimization

The identification of multiple bad data, especially when mutually interacting, may be difficult to handle, since the well known procedures based on the normalized or weighted residuals may become faulty. In such a case, successive elimination of the measurement with the largest normalized residual may result in the suppression of correct measurements instead of the bad data. Then the problem of identifying bad data is considered as a combinatorial decision procedure. In this paper, binary PSO is used for the identification of multiple bad data in the power system state estimation. The proposed binary PSO based procedures behave satisfactorily in the identifying multiple bad data. The test is carried out with reference to the IEEE-14 bus system.