Bad data identification in power system state estimation based on measurement compensation and linear residual calculation

A method of bad data identification is described. The method introduces several new concepts as well as utilizing the advantages of the combinatorial optimization and hypothesis testing identification approaches. It first sequentially eliminates suspected measurements until no gross errors remain in the measurement set and then performs the final identification by analyzing values of estimated errors of the suspected measurements. The vector of normalized residuals is obtained after each elimination without re-estimation, which results in high computational speed. The measurement removal is efficiently performed by special techniques, namely, measurement compensation and linear residual calculation, which are described in detail. The estimated errors of the suspected measurements are automatically available upon completion of the elimination process. The method reliably identifies multiple interacting bad data. The results of testing the algorithm in a simulated energy management system (EMS) environment are reported