基于小波分析的电力系统故障时空检测与诊断

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (12): 3018-3024.

• 信息、控制、决策与仿真 • 上一篇下一篇

基于小波分析的电力系统故障时空检测与诊断

李刚¹, 许鹏程², 韩龙美¹

1.华北电力大学控制与计算机工程学院,河北保定 071003;
2.华北电力大学电气与电子工程学院,北京 102206

收稿日期:2015-05-27 修回日期:2015-08-12 出版日期:2015-12-08 发布日期:2020-07-30
作者简介:李刚(1980-),男,河北枣强,博士,讲师,研究方向为软件工程、电力系统仿真。
基金资助:
国家自然科学基金资助(51407076); 河北省自然科学基金(F2014502050); 河北省高等学校科研项目(Z2013007); 中央高校基本科研业务费专项资金(2015ZD28)

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

Li Gang¹, Xu Pengcheng², Han Longmei¹

1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Received:2015-05-27 Revised:2015-08-12 Online:2015-12-08 Published:2020-07-30

摘要/Abstract

摘要： 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。

关键词: 故障诊断, 小波分析, 奇异性检测, 模极大值, 高、低频系数

Abstract: 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.

Key words: fault detection, wavelet analysis, singularity detection, modulus maxima, high and low frequency coefficients

中图分类号:

TP391.9

李刚, 许鹏程, 韩龙美. 基于小波分析的电力系统故障时空检测与诊断[J]. 系统仿真学报, 2015, 27(12): 3018-3024.

Li Gang, Xu Pengcheng, Han Longmei. Fault Spatial-temporal Detecting and Diagnosis for Power Grid Based on Wavelet Analysis[J]. Journal of System Simulation, 2015, 27(12): 3018-3024.

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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

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