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

Wen Xu,Xing Xie,Chun Huang

doi:10.1109/access.2020.3000258

Wen Xu, Xing Xie + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3000258

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Abstract

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.

Highlights

Fast and robust techniques for fundamental frequency positive sequence (FFPS) detection have played an important role in applications such as grid harmonic compensation and control of the grid connected converter for distributed energy and island detection.A large number of new algorithms for FFPS estimation have been proposed in recent years under different steadystate and dynamic conditions
An analysis of this curve leads to the following conclusions: Regardless of the order of modified TKF (MTKF), the characteristic reflected by r1 extracts the FFPS component and eliminates the other harmonics when the grid frequency is at its nominal value and all of the harmonic models are included in the transition matrix
This is due to the delay caused by the moving average filter (MAF) prefiltering stage and the slight error caused by frequency offset that may lead to the incorrect direction of the correction

Summary

INTRODUCTION

Fast and robust techniques for fundamental frequency positive sequence (FFPS) detection have played an important role in applications such as grid harmonic compensation and control of the grid connected converter for distributed energy and island detection. The frequency response of Tk KF (the kth order Taylor Kalman Filter) can be obtained by taking z transform of its update state equation that is derived in the appendix to save space An analysis of this curve leads to the following conclusions: Regardless of the order of MTKF, the characteristic reflected by r1 extracts the FFPS component and eliminates the other harmonics when the grid frequency is at its nominal value and all of the harmonic models are included in the transition matrix. The Taylor-based Kalman filter with an accurate model and higher order has larger flat regions in its frequency response, enhancing its estimation performance under the frequency deviation condition. Inspired by the introduction of the suboptimal scaling factor in [32] to improve

COMPUTATIONAL COST

CONCLUSION