A Robust Statistical Approach to Distributed Power System State Estimation With Bad Data

This paper presents a robust statistical approach to distributed power system state estimation (DPSSE) under bad data based on iterative reweight least squares (IRWLS) method and an improved alternating direction method of multipliers (ADMM) framework. In particular, the Hampel's redescending and the Schweppe-Huber generalized M-estimators (SHGM) are studied for mitigating the adverse effect of outliers with large magnitude. Moreover, a new robust weight smoothing scheme is proposed for improving the numerical stability and convergence speed of the algorithm. The proposed approach is further extended to recursive monitoring of measurement devices and inpainting of missing data by utilizing prior information provided in previous state estimation. The resultant algorithm is solved using the Levenberg-Marquardt (LM) solver, which helps to maintain numerical stability under unexpected adverse situations. Experimental results show that the proposed approach outperforms conventional approaches using the ADMM with L1 outlier detection in state estimation accuracy and convergence speed. Moreover, it maintains numerical stability and good performance under missing data. As state estimation will be performed more frequently in future smart grid due to the increased penetration of renewables, the proposed methods and investigations offer much insight in addressing the missing data and outlier problems in DPSSE.