基于强跟踪泰勒扩展卡尔曼滤波的暂态电能质量检测

A Transient Power Quality Detection Method Based on Strong Tracking Taylor Extended Kalman Filter

摘要: 本文研究基于强跟踪泰勒扩展卡尔曼滤波(STEKF)的暂态电能质量信号检测方法。针对泰勒卡尔曼滤波(TKF)算法状态空间的维数过大以及无法快速准确跟踪突变信号的缺点，将泰勒扩展卡尔曼滤波(TEKF)与强跟踪滤波(STF)相结合，先对幅值和相位分别进行2阶泰勒展开，再借助渐消因子根据实际残差与理论残差实时调整TEKF中的误差协方差矩阵，从而增强算法对突变信号的跟踪能力。对解析调幅信号和调相信号、Simulink搭建的三相系统故障下的电压暂降信号和三相系统整流谐波信号四种情况进行了仿真实验。实验结果表明，与TEKF算法相比，本文提出的STEKF算法具有更快的跟踪速度和更高的跟踪精度。

Abstract: A transient power quality signal detection method based on strong tracking Taylor extended Kal-man filter (STEKF) is studied in this paper. The Taylor Kalman filter (TKF) algorithm used for transient power quality signal detection is not only big in the number of state variables that cause large dimension of state transition matrix of the problem, but fails to track mutation signal quickly and accurately. In this paper, the Taylor extended Kalman filter (TEKF) and strong tracking filter (STF) are combined together to deal with these problems. First, amplitude and phase angle signals are truncated at the second order Taylor expansion separately. Then, with the help of a scaling factor, the covariance matrix is adjusted so that the ability of the algorithm tracking mutation signal can be enhanced. Four kinds of signals are simulated, including amplitude modulation signal, phase modulation signal, voltage sag fault signal and rectifying harmonic signal. The former two signals are generated by analytical functions and the left two are generated by a three-phase system built in Simulink. Simulation results show that the proposed STEKF algorithm is faster in tracking speed, higher in measurement accuracy than the TEKF algorithm.