摘要
现有文献的故障监测与定位小波算法都是在录波采样频率相同的前提下进行的,配电网小电流接地故障实时检测,与变电站接地故障检测的环境完全不同,配网故障监测装置,采用不同标准采样频率(3 600 Hz、4 800 Hz)上送实时故障录波数据到配网中心,更有采用4 096 Hz的采样频率上送故障录波数据,在配网中心需要把不同采样频率的录波数据进行分析计算,提取故障特征、检测故障,确定故障首尾端,实现故障检测与定位。本文给出了小波能量特征的定义、不同采样频率能量特征的折算、基于能量特征的配电网接地故障监测与定位算法。多种采样频率下配网接地故障检测与定位,通过小波变换提取故障能量特征、将不同采样频率故障录波信号,折算到最低采样频率下的能量特征,然后根据能量特征来判别故障类型、确定故障首尾端。多采样频率的小波能量特征折算算法,对于类似的小波变换的使用场合,也有借鉴意义。
The fault monitoring and location wavelet algorithm in the existing literature is carried out on the premise of the samerecording frequency, and the real time detection of the small current grounding fault in the from the grounding fault detection environment of the substation, the distribution network fault monitoring device uses the differentstandard sampling frequency(3 600 Hz,4 800 Hz)to send the real time fault record. Wave data to use 4 096 Hz sampling frequency to send fault recorded wave data, in the distribution network center needs to analyze and calculate therecording data of different sampling frequencies, extract fault features, detect faults, determine the head end of the fault, adetection and positioning. This paper gives the definition of the wavelet energy characteristics, the conversion of different sampling frequency energy characteristics, and the ground fault monitoring and positioning algorithm based on the energy characteristics in the distribution network. The fault detection and location of the grounding fault in the distribution the energy characteristics of the fault energy are extracted by the wavelet transform, and the energy signals of different sampling frequencies are converted to the lowest sampling frequency, then according to the characteristics of energy todetermine the fault type, fault to determine the head and tail end. Wavvlet energy feature conversfrequency is also useful for similar applications of wavelet transform.
作者
齐文斌
QI Wenbin(Beijing Sifang Automation Co., L td . , Beijing 100085,China)
出处
《电力大数据》
2018年第5期59-65,共7页
Power Systems and Big Data
关键词
接地故障检测
小波分析
多采样频率
小波能量特征
配电网
ground fault detection
wavelet analysis
multi sampling frequency
wavelet energy feature
distribution network