Bad data detection and handling in distribution grid state estimation using artificial neural networks

Abstract

This research project addresses the problem of erroneous measurements for distribution grid state estimation (DGSE) by using bad data detection. A method based on artificial neural networks is developed and tested in combination with DGSE. The necessary steps in order to create the neural networks are presented and training parameters are investigated. The method replaces identified measurement errors with new estimates. The developed method is validated by simulation on the basis of an electric distribution grid. It is shown that the bad data correction method detects and correctly identifies single and multiple erroneous measurement values. Furthermore, the ability of the developed method to handle different types of measurement errors and the impact on the quality of the state estimation result are examined. The method improves the quality of the DGSE result and reduces the state estimation's probability to diverge.