A Bad Data Identification Method Based on Optimal Weight-Tuning for Power System State Estimation

The weighted least squares (WLS) method has been widely used in power systems for state estimation (SE), but in the presence of gross errors, the results of WLS SE can be significantly biased. While various bad data identification (BDI) methods exist, identifying multiple bad data for SE is still challenging, especially when bad data located at leverage points can disproportionately corrupt SE accuracy. This paper proposes an Optimal-Weighted Least Squares State Estimation (OWLS-SE) method with an optimal weight-tuning strategy to address these challenges. The impact of bad data at leverage points on estimation errors is first mathematically demonstrated through a theoretical upper bound of the SE relative error. Then, the optimal dynamic weights are developed to minimize this error bound by quantifying leverage point impacts and integrating them into a multi-objective optimization framework. The proposed OWLS-SE achieves simultaneous SE and BDI within a single optimization model, eliminating conventional two-stage processing. Numerical experiments on the 6- bus system, IEEE 30-, 118- bus systems, and the 2869- bus PEGASE system demonstrate that OWLS-SE achieves superior BDI and SE accuracy with robust performance even when multiple gross errors occur at leverage points, while maintaining computational efficiency comparable to the simplest WLS method.