Using Branch Current Measurements for Parameter Identification in Extended Kalman Filter based Distribution System State Estimation

Abstract

Driven by increased penetration from distributed generation, distribution networks require improved operational state awareness tools in the presence of low measurement redundancy. This can be achieved by utilizing Kalman filter based state estimation in case process noise covariance matrix is optimally assessed. This paper aims to investigate the possibility of using readily available conventional branch current flow measurements to assess process noise covariance matrix in extended Kalman filter (EKF) based state estimation for distribution networks. The process noise covariance matrix has a significant impact on EKF's performance. Recently, a method for optimizing the process noise covariance matrix is proposed leveraging the correlation between the estimation error and the cost function via the innovations of branch power flow measurements. This paper extends that to include the innovations of branch current flow measurements in the cost function. Performances of the proposed approach are evaluated on the modified IEEE 13- and IEEE 37-bus distribution test systems. It is demonstrated that the proposed method is robust to different loading conditions and different measurement configurations. Comparison results with the weighted least square estimator show that our method achieves significantly improved estimation accuracy.