A comparison of MV Distribution System State Estimation methods using field data

This paper compares the performance of five different Distribution System State Estimation (DSSE) methods, using field data taken from a European MV distribution network. The performance of each method is assessed in terms of its solution accuracy, robustness to noise and input measurement uncertainty, and ability to identify bad data and network topology errors. The advantages and disadvantages of each approach are discussed with regard to their application to static state estimation in MV distribution systems, where the quantity and quality of available network measurements is typically low. The Weighted Least Squares (WLS) approach is by far the most widely-used method in this context. However, the results from this study demonstrate that Extended Kalman Filter (EKF) techniques have significant advantages, particularly in terms of their ability to handle various types of input data errors. The performance of robust solution methods for distribution system state estimation is also compared.