Enhanced Bad Data Identification in Distribution System State Estimation

Abstract

State estimation is an important tool in monitoring and controlling active distribution systems. An important partner of estimation is bad data identification, which could effectively improve the estimation's precision in the cases where bad data exists in the measurement set. Once the measurement set is detected to include bad data, its spot must be identified to be excluded from the estimation problem. It is well known that the biggest value of residual vector is most likely corresponding to the bad data. This paper proposes a new estimation algorithm, which enhances the influence of the bad data in the residual vector, increasing the possibility of the successful bad data identification based only on residual vector.