Sequential bad data analysis in state estimation using orthogonal transformations

The sequential identification of multiple bad data in power system state estimation using orthogonal transformations is described. The method involves iteratively building a list of suspect bad data based on their normalized residuals. The measurements are then analyzed for their estimated errors, and the suspect list is pruned to reveal the bad data. Valid measurements are then returned to the system for completing the solution. As part of this development, a new method of computing and updating the residual covariance matrix is also presented. Test results on the IEEE 30-bus system are presented.<
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