Application of Extended Fractional Kalman Filter to Power System Dynamic State Estimation

It is known that power system dynamic state estimation could predict the future state of the system accurately. In order to analyze the trend of power grid operation status more rigorously, the calculation precision still require considerable increase in the prediction step of the traditional filter methods and its improvements. In this paper, The power system dynamic state estimation based on the modified extended fractional Kalman filter is proposed. The fractional and extended fractional Kalman filter models are introduced in advance, respectively. Based on the fractional order systems state estimation, the model of the modified extended fractional Kalman filter is established and applied to the dynamic state estimation of power system by fully considering the characteristics of power system. The test results show that the proposed method takes better account of the redundant measurement snap information obtained from the actual mixed measurement system. It can exactly track the real-time variation of state variables, besides, the predicted value of power system operation status is increased obviously.