Simultaneous Input, Parameter, and State Estimation for Multi-Machine Power Systems with Detailed Generator Model

This paper presents a novel method for Dynamic State Estimation (DSE) in multi-machine power systems, focusing the application of the Unscented Kalman Filter (UKF) in condition of uncertain input and parameter. The previous UKF-based DSE studies frequently assumed complete knowledge of the system inputs and parameters. In this work, a robust UKF-based framework is proposed to deal with uncertainties in both inputs and parameters. This method is applied to a multi-machine power system with sub-transient synchronous generator models. The framework incorporates a novel technique for parameter identification and adaptive filtering, which improves the robustness and accuracy of state estimation. The effectiveness and robustness of the suggested strategy are confirmed thoroughly by simulations and comparative investigations, indicating its potential to handle the challenges of the actual world power system operation. The results show that UKF is reliable for simultaneous state, input and parameter estimation under different noise and disturbance scenarios.