基于Sigma点卡尔曼滤波器的电力频率跟踪新算法

Frequency Tracking of Distorted Power Signal Using Complex Sigma Point Kalman Filter

通过变换,首先将三相电压信号转换成一复电压信号,再利用一种复数型Sigma点卡尔曼滤波(CSPKF)算法以改进对发生谐波畸变和随机噪声干扰的电力系统电压信号的频率进行动态估计和跟踪的过程。理论证明,CSPKF算法与现有的复数型扩展卡尔曼滤波(ECKF)算法相比具有更佳的跟踪精度和稳定性。此外,CSPKF算法还成功解决了所有卡尔曼滤波算法都必须面对的当算法收敛后,系统参数发生突变的情况下需要重置误差协方差矩阵来重新跟踪这些变化的问题,进一步提高了其跟踪速度。对几种暂态电力信号模型的算法仿真表明,CSPKF算法具有优异的动态跟踪性能,迅速跟踪频率和幅值变化的同时又保持了较低的跟踪误差。

A complex Sigma point Kalman filtering （CSPKF） algorithm for the improvement of frequency estimation of distorted power system signals in the presence of random noise and harmonic disturbance is presented. The 3-phase voltage signal of a power system is transformed into a complex voltage signal for the processing of the proposed algorithm. CSPKF outperforms the popular extended complex Kalman filter （ECKF） in terms of accuracy and stability. Moreover, it＇s not necessary to reset the covariance matrix for CSPKF as it is for all Kalman filter algorithms to track sudden parameter changes after initial convergence, thus significantly improving its tracking speed. Numerical simulations show that the proposed CSPKF algorithm possesses an excellent dynamic frequency tracking characteristic while maintaining a considerably low tracking error.