A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation

This paper introduces a novel hybrid method for Power System State Estimation (PS-SE) that effectively integrates the strengths of Weighted Least Squares (WLS) and the Extended Kalman Filter (EKF) through an adaptive weighting mechanism. The proposed method addresses key challenges in modern PS-SE, including measurement uncertainties, bad data detection and handling, and convergence reliability. By incorporating an adaptive weighting mechanism, the hybrid approach dynamically adjusts estimation parameters based on the quality of the measurements, enabling it to maintain high accuracy for clean data while demonstrating exceptional resilience against outliers and noisy measurements. The performance of the proposed method is rigorously evaluated against established state estimation techniques, including WLS, EKF, Bayesian method, Huber-Adaptive Method (HAM) and Neural network-based Method. Simulations are performed on IEEE 14-bus, IEEE 34-bus and IEEE 342-bus test systems to assess estimation accuracy, convergence behavior, computational efficiency, and robustness in the presence of bad data. Results highlight the superior performance of the hybrid method, which achieves higher accuracy and robust convergence properties while requiring 40% fewer iterations than conventional WLS. Despite its enhanced capabilities, the computational burden remains comparable to traditional techniques, making it highly suitable for real-time applications. These findings underscore the proposed hybrid method as a significant advancement in power system state estimation, offering a reliable, efficient, and robust solution for modern power system monitoring and control. It represents a promising approach to address the increasing complexity and data uncertainties in contemporary power grids.