Multi Bad Data Identification Based on Evidence Fusion Theory
Yang Lijun · Power System Technology · 2012
To overcome the occurrence of residual pollution and residual submerge in multi bad data identification effectively,Electric distance,correlation coefficient and sensitivity as evidences are introduced.The measurement correlative degree is determined by evidence fusion theory which can reflect the possibilty of residual pollution and submerge occurrence in measured data.There is smaller possibility of residual pollution occurrence in the data with larger residual and lower measurement correlative degree,so the data can be directly identified as bad data;for the data with larger residual and higher measurement correlative degree the bad data should be isolated by fuzzy clustering,then the system should be partitioned and bad data is modified step by step,and then reliable measured data can be chosen for the state reestimation.The effectiveness of the proposed method is verified by simulation results of IEEE 14-bus system.