Dynamic State Estimation of Power Systems with Uncertainties Based on Robust Adaptive Unscented Kalman Filter

In this study, a robust adaptive unscented Kalman filter (RAUKF) is developed to mitigate the unfavorable effects derived from uncertainties in noise and model. To address these issues, M-estimator first utilized update measurement covariance. Next, deal with of model parameter errors while considering computational complexity real-time requirements dynamic state estimation, an method produced. The proposed integrated spherical simplex transformation technology, then novel derivative-free track dynamically states power system against uncertainties. Finally, effectiveness robustness are demonstrated through extensive simulation experiments on IEEE 39-bus test system. Compared other methods, can capture characteristics synchronous generator more reliably.