A ROBUST HYBRID POWER SYSTEM STATE ESTIMATOR WITH UNKNOWN MEASUREMENT NOISE

In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of supervisory control and data acquisition (SCADA) and PMU measurements are quite different, causing time skewness problem. In this chapter, we propose a robust state estimation framework to address the unknown non‐Gaussian noise and the measurement time skewness issue. In the framework, the Schweppe‐type Huber generalized maximum likelihood (SHGM)‐estimator is advocated for SCADA measurements‐based robust state estimation. We show that the state estimates provided by the SHGM‐estimator follow roughly a Gaussian distribution. This allows us to effectively combine it with the buffered PMU measurements for final state estimation. The robust Mahalanobis distances are proposed to detect outliers and assign appropriate weights to each buffered PMU measurement. Those weights are further utilized by the SHGM‐estimator to filter out non‐Gaussian PMU measurement noise and help suppress outliers. Extensive simulation results carried out on the IEEE 30‐bus test system demonstrate the effectiveness and robustness of the proposed method.