杨宏宇,吕万,关雅静,陈曦,吴熙.基于高阶累积量与ESPRIT算法的功率振荡检测方法[J].电测与仪表,2021,58(1):37-45. Yang Hongyu,Lyu Wan,Guan Yajing,Chen Xi,Wu Xi.Power Oscillation Detection Method Based on Higher Order Cumulant and ESPRIT Algorithm[J].Electrical Measurement & Instrumentation,2021,58(1):37-45.
基于高阶累积量与ESPRIT算法的功率振荡检测方法
Power Oscillation Detection Method Based on Higher Order Cumulant and ESPRIT Algorithm
电力系统实测信号在传输的过程中可能出现异常数据，且包含大量的高斯白噪声和高斯有色噪声，对功率振荡检测结果的精确性产生了较大的影响。为实现功率振荡信号的准确检测，本文首先对信号预处理方法进行了改进，采用七点二阶算法前推差分公式对异常数据进行识别，并进行了补正、滤波和去直流处理。进一步地，将原始信号用四阶累积量代替，并使用旋转不变技术信号参数估计(Estimating Signal Parameter via Rotational Invariance Techniques, ESPRIT)算法进行辨识，实现对功率振荡信号的精准检测。仿真结果表明，相比于直接使用ESPRIT算法，本文提出的改进检测方法可以有效地消除异常数据、高斯白噪声和有色噪声对信号分析的影响，从而更为准确地辨识出系统中的振荡信息。
英文摘要:
The measured signals of power system may have abnormal data, Gaussian white noise and colored noise during the transmission process, which may affect the accuracy of power oscillation detection result. In order to realize accurate detection, the signal preprocessing method is firstly improved using seven-point second-order algorithm to identity the abnormal data. This step also includes correcting abnormal data, filtering and direct current removal. Furthermore, the ESPRIT identification algorithm is used to analyze the fourth order cumulant instead of the original signal, thereby realizing the precise detection of the power oscillation. The simulation results show that compared with the direct use of ESPRIT algorithm, the improved detection method in this paper can effectively eliminate the influence of system Gaussian white noise and colored noise on signal analysis, and the oscillation information in the system can be identified more accurately.
