A neural network preestimation filter for bad-data detection and identification in power system state estimation

Popular state estimation techniques in industry are mostly based on the weighted least squares (WLS) method and its derivatives. These estimators usually detect and identify multiple gross measurement errors by repeating a cycle of estimation-detection-elimination. It is rather time consuming for large systems. This paper presents a neural network preestimation filter to identify most forms of gross errors, including conforming bad data, in raw measurements before state estimation rather than afterwards. The proposed neural network model is trained to be a measurement estimator by using the correct measurements of typical system operating states. Once trained, the filter quickly identifies most forms of gross measurement errors simultaneously by comparing the square difference of the raw measurements and their corresponding estimated values with some given thresholds. System observability is maintained by replacing bad data with their reasonably accurate estimates. Using the proposed neural network preestimation filter, the efficiency of present state estimators is greatly improved. Results from several case studies are presented.