由于线路故障位置的不确定性,目前串联型故障电弧检测方法主要基于电流信号分析进行识别.通过对不同负载在串联型故障电弧发生前后的电流波形进行对比,得出了串联型故障电弧电流特性及其变化规律;以串联型故障电弧的电流信号为研究对象,介绍了基于希尔伯特黄变换、信息熵与短时傅里叶变换、小波近似熵与支持向量机的串联型故障电弧检测方法,概述了不同检测方法的故障电弧特征提取过程;对3种串联型故障电弧检测方法优缺点进行了比较,指出基于希尔伯特黄变换、信息熵与短时傅里叶变换的检测方法可有效提取故障电弧发生时电流的时频特性,对提取的时频谱幅值设置合适的阈值即可作为串联型故障电弧识别的依据,但准确性和实时性不高,而基于小波近似熵与支持向量机的检测方法可直接提取近似熵作为支持向量机的输入来识别串联型故障电弧,具有较高的准确性和实时性,更适用于煤矿现场.%For uncertainty of line fault location,current series fault arc detection methods are mainly based on current signal analysis.By comparing current waveforms before and after series arc fault under different loads,characteristics and regularities of series fault arc current were obtained.Taking current signal of series fault arc as research object,three kinds of series fault arc detection methods were introduced which use Hilbert-Huang transform,information entropy and short-time Fourier transform and wavelet approximate entropy and support vector machine respectively.Extraction processes of fault arc feature with different detection methods were summarized,and advantages and disadvantages of the three methods were compared.A view was pointed out that the detection method based on Hilbert-Huang transform and the one based on information entropy and short-time Fourier transform can effectively extract time-frequency characteristics of fault arc,and series fault arc can be identified according to proper threshold of time-frequency spectrum amplitude with low accuracy and real-time performance.The detection method based on wavelet approximate entropy and support vector machine can directly extract approximate entropy as input of support vector machine to detect series fault arc with higher accuracy and real-time performance,which is more suitable for coal mine.
展开▼
