基于抗差最小均方估计的输电线路参数辨识

输电线路数学模型广泛运用于电力系统分析计算中，其参数的准确性与电网的安全稳定运行密切相关，广域测量系统的发展为获取输电线路参数提供了新的手段。针对目前参数辨识算法缺乏测量误差对辨识结果影响的研究，提出基于抗差最小均方估计（robust least mean squares，RLMS）的输电线路参数辨识算法，该算法以抗差函数代替传统最小均方估计算法中的均方误差，并通过自适应阈值法调节阈值，进而使得辨识算法在抗噪声方面具有较强的适应能力；对不同时刻的计算结果，提出了基于核密度估计和点估计法提取结果的统计特征，最后通过仿真分析与实测数据对比验证了所述算法的有效性。

Transmission Line Parameters Identification Based on Robust Least Mean Squares

The mathematical model of transmission lines is widely used in the analysis and calculation of power system； and the accuracy of its parameters is closely related to the safe and stable operation of power grid. The development of wide area measurement system （WAMS） supplies a new way to obtain the parameters of transmission lines. According to the status that there were few studies on the influence of measurement error on the results of parameter identification algorithm, this paper proposed a parameter identification algorithm for transmission lines based on robust least mean squares. The algorithm was introduced the robust criterion function instead of mean square error in traditional minimum mean square estimation algorithm, and adjusted the threshold value through adaptive thresholding method, which could make it have strong adaptability of noise resistance. Meanwhile, kernel density estimation method and point estimation method were used to extract the statistical feature of calculation results at different time. Finally, the comparison between simulation analysis and measured data verifies the effectiveness of proposed algorithm.