Generalized Loss Based Geometric Unscented Kalman Filter for Robust Power System Forecasting-Aided State Estimation

Generally, the power system (PS) faces various anomalies such as non-Gaussian measurement noises and gross measurement errors, when the communication link is lost or the phasor synchronization is inaccurate. Therefore, the robust forecasting-aided state estimation (FASE) is crucial for PS stability. This letter develops a novel geometric unscented Kalman filter (GUF) with the generalized loss (GL) (GL-GUF) to estimate PS state for forecasting aid. In contrast to the minimum mean square error (MMSE) criterion in the original GUF framework, GL-GUF combines the strength of the GL in robust information learning against non-Gaussian disturbances and the advantages of GUF in handling strong model nonlinearity with high accuracy and good stability. More importantly, by establishing the linear regression model to obtain residual vectors and introducing the negative log-likelihood of GL, simultaneously, GL-GUF optimizes free parameters to avoid manual parameter tuning. Simulations on IEEE 14-bus and 30-bus test systems confirm the high precision and robustness of GL-GUF for non-Gaussian disturbances.

Automated summary

This letter proposes a novel geometric unscented Kalman filter (GUF) with generalized loss (GL) to estimate the state of the power system (PS) for forecasting aid. The GL-GUF combines the strength of robust information learning against non-Gaussian disturbances and the advantages of GUF in handling strong model nonlinearity with high accuracy and stability. Simulations on IEEE 14-bus and 30-bus test systems confirm the high precision and robustness of GL-GUF for non-Gaussian disturbances.