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Current state estimators employ the Weighted Least Squares (WLS) estimator to solve the state estimation problem. Once the state estimates are obtained, Chi-Square test is commonly used to detect the presence of bad data in the measurement sets. Regretfully, this test is not entirely reliable, that is, bad data existing in the measurement set could be missed for certain cases. One reason for this is the approximations used to compute the bad data suspicion threshold, which is set based on an assumed chi-squares distribution for the objective function. In this paper, a modified metric is proposed in order to improve the bad data detection accuracy of the commonly used chi-square test. The bad data detection performance of the proposed test is compared with that of conventional chi-square test.
Göl et al. (2015) studied this question.