Abstract
A novel and simple approach to bad data identification and removal is suggested. The method utilizes the framework of decision theory. It does not require additional runs of state estimation and it is powerful to identify the bad data. A doubtful bad data set can be found by residual test detection. A decision table and a penalty matrix for removing good data and for failure to remove bad ones are defined and constructed. An optimal removal decision could be made as the minimized expected penalty criterion with posterior probability density. More than one hundred cases with different combination of bad data included the ‘interacting’ bad ones have been tested on a testing system. The results are satisfactory.
Published Version
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