Bad data detection in power system state estimation based on generalized likelihood ratio test

The state estimator is a fundamental algorithm used in modern power industry to estimate the steady state of the system. The gross errors in input data of power system SE algorithm can considerably effect the accuracy of estimated measurements. Although, the commonly used state estimator algorithms utilize some statistical tests to detect the bad data, they are unable to properly deal with the gross errors with smaller magnitude and higher probabilities. This paper develops a new procedure based on the GLR test to effectively detect the bad measurements in the SE algorithm. The proposed strategy can detect, identify and to estimate the size of gross errors. This method is implemented on both single and multiple bad measurements cases to comparatively investigate the abilities of this algorithm. The obtained results confirm that the proposed algorithm has greater power of detecting and identifying the bad measurements compared to the estimator based on the conventional Chi-square and normalized residual test statistics. The IEEE-14 bus test system has been used to validate the theoretical results in this work and its practical implementation in state estimation problems.