Abstract
The authors consider the online implementation of a general, reliable and efficient bad-data analysis procedure for power system state estimation. It is based on hypothesis testing identification, which was previously proposed and subsequently improved by the authors. The procedure involves a sequential measurement error estimator along with adequate sparsity programming techniques. Both make the procedure easy to implement on any state estimator. A criterion for multiple noninteracting bad-data identification is also proposed, which is applicable to any bad-data analysis method. Simulations are reported on systems of up to 700 buses. A thorough comparison with classical methods is also included. >
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