GIS局部放电特高频信号波形分析与特征参量提取

Waveform Analysis and Feature Extraction of Partial Discharge UHF Signals in Gas Insulated Switchgear

采用高采样率数字检测设备采集特高频(UHF)信号的波形,通过对UHF信号波形分析可实现多源放电分离。为此提出了一种局部放电UHF信号波形分析方法:采用高斯平滑滤波算法获得UHF信号的包络线,提取其上升时间、下降时间等特征参量;提取过零率参量用于表征UHF信号的振荡特性;利用数学形态学梯度方法表征累积能量函数的上升陡度,提取最大上升陡度参量。将提取的特征参量应用于气体绝缘组合电器(GIS)内混合缺陷的多源放电分离。在GIS内建立了尖端、自由微粒、悬浮电极及盆式绝缘子表面附着金属丝4种典型缺陷,并利用4种缺陷的UHF信号测试了特征参量的可分性;利用模糊最大似然法对特征参量进行聚类,实现了GIS内尖端悬浮及尖端沿面2组混合UHF信号的有效分离。结果表明,该波形分析方法表征了局部放电UHF信号的主要特征,提取的特征参量对GIS典型缺陷的UHF信号具有良好的可分性,并可有效分离混合缺陷产生的局部放电信号。

Ultra high frequency （UHF） method gradually becomes an effective approach for partial discharge （PD） detection in gas insulated switchgear （GIS）. The waveform of UHF signals can be acquired by digital detection instruments with a high sampling rate. By analyzing the waveform characteristics of UHF signals, the extracted features can be applied to separation of mixed signals originated from multiple PD sources. Consequently, we proposed a signal feature extraction algorithm based on waveform analysis technique. The envelope curves of UHF signals were obtained by using the Gaussian smoothing method, and some features such as rising time and falling time were extracted. The zero-cross rate was extracted to represent the oscillating characteristics of UHF signals. Moreover, the mathematical morphology gradient （MMG） of cumulative energy function was calculated to characterize its rise steepness. Thereafter, the extracted features were applied to separation of mixed UHF signals. Experiments on four typical defects, including protrusion, free-moving particle, floating electrode, and metal wire, on spacer were performed, and the classification performances of the extracted features were tested with the acquired signals. By clustering the extracted features with the fuzzy maximum likeli- hood algorithm, mixed UHF signals of two multi-defect models were successfully separated. The results suggest that the proposed method is effective for representing UHF signal characteristics. The extracted features can accurately classify UHF signals generated by different typical defects in GIS, and can effectively separate the PD signals of multiple defect sources.