Analysis and Application of Taylor-Kalman Filters Under a Distorted Grid Condition

The Taylor-Kalman filter (TKF) is a linear filter that can be modeled using either an accurately modeled TKF (known as the modified TKF) or an approximately modeled TKF. The higher-order TKF can not only adapt to the small frequency deviation condition but also prevents certain harmonics leakage. It has been increasingly widely applied in power grid synchronization and other power system fields. However, to the best of authors' knowledge, systematical analyses of the impact of different models on the estimation performance and the methods for extraction of the shifted grid frequency have not been reported to date. The aim of this paper is to examine the TKF-based phase estimation algorithms (PEA) with regard to aspects such as the steady state, dynamic state and computational cost. Comparisons were carried out to evaluate the effects of the model and the order of TKF on the dynamic response and steady-state error. A strong tracking algorithm was also introduced to enhance the dynamic response. Several approaches for reducing the computational burden are given. Finally, combined with the moving average filter (MAF), which is a typical low-pass filter, an application example of TKF-based PEA was developed, and its performance was verified by experiments.