低频采样下基于卡尔曼滤波的同步相量测量算法的研究

各种基于定间隔采样的传统相量测量算法在跟踪速度和测量精度上不能很好地统一，在低频采样情况(每周波4~8个采样点)下提出一种基于线性卡尔曼滤波技术的10状态卡尔曼滤波模型，用于实时跟踪电力系统的电压有效值、频率、频率变化率和初相角，为系统提供精确的参数。结合二元泰勒展开公式，推导出各个参数的计算公式，并依据信号模型选取合适的时间区间保证算法对突变信号的响应速度。从采样频率、数据窗持续时间和算法对突变信号的跟踪精度方面对算法进行了讨论，结果表明使用该算法可以在低频采样下获得较高的参数估计精度。

A Kalman-filter based phasor measurement algorithm under low sampling frequency

In consideration of unfavorable integration between tracking speed and measurement precision of conventional phasor measurement methods based on various constant-interval sampling, a ten-state linear Kalman-filter model for tracking power system RMS voltage, frequency, frequency variation rate as well as initial phase angle under the condition of low sampling frequency (4~8 sampling points per cycle) is presented. The algorithm is derived using the second-order Taylor expansion formula, and then the formulas of parameters above are established. According to the signal model, an appropriate time interval is selected to guarantee the response speed of algorithm to signal mutation. Sampling frequency, data window duration, and tracking precision to mutation signals are studied. Simulation results show that the algorithm has higer parameter estimation accuracy at low sampling frequency.