基于小波分析的电力系统故障时空检测与诊断

Fault Spatial-temporal Detecting and Diagnosis for Power Grid Based on Wavelet Analysis

现代电力系统监测数据的异常或故障信号往往隐藏在大数据集合中,且关联性较强,传统傅里叶变换分析方法不具有时域局部化分析能力。利用小波分析的时-频特性,给出一种电力系统暂态信号的奇异性检测算法。通过对暂态信号的多尺度一维小波分解,提取其低频系数与高频系数,对信号去噪的同时得到故障时刻信息;基于模极大值的奇异性检测,获得故障点的定位信息,从而实现异常信号的时空检测与诊断。在IEEE 39节点系统中的仿真结果表明,该方法实现了对奇异信号的时-频特征分检,初步满足了电力系统故障时空定位的要求。

The monitoring data of modern power system have strong intrinsic relationship and its abnormal and fault signal is often hidden in big data sets, so the traditional Fourier transform analysis methods don't have the ability of time domain localization analysis. A new method based on time-frequency characteristics of wavelet analysis was proposed for singularity detection of transient signals in power system, which through decomposing the multi-scale one-dimensional transient signal wavelet, extracted high frequency and low frequency coefficient, and got fault time information while the signal de-noising; then based on the singularity detection of modulus maxima, the fault location information was obtained, so as to realize the temporal-spatial detection and diagnosis of abnormal signal of power system. By means of the simulation analysis in IEEE 39 bus system, the results show that the proposed method realizes the singular signal time-frequency characteristics of sorting, and initially satisfies the requirement of fault spatial-temporal positioning.