Unscented Kalman Filter with Generalized Correntropy Loss Combined with Gross Error Detection and Identification for State Estimation of Power System

The measurement values would be affected by different types of gross errors, which will reduce the accuracy of state estimation of power system. To solve these problems, this paper proposes a generalized Correntropy loss based unscented Kalman filter combined with gross error detection and identification (UKF-GCL-GEDI).UKF-GCL-GEDI method can monitor the gross error in the measurement data in real time, classify the types of gross errors, estimate the true measured values, and then compensate the abnormal measurement points, so as to improve the accuracy of state estimation. Compared with unscented Kalman filter with generalized Correntropy loss and unscented Kalman filter, UKF-GCL-GEDI has higher robustness and can handle different types of gross errors in measurement data. The simulations on IEEE 6-bus test system show that UKF-GCL-GEDI can effectively suppress the influence of gross error.