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机构地区:[1]固原供电局,宁夏固原756000 [2]西安供电局,陕西西安710032 [3]新能源电力系统国家重点实验室(华北电力大学),北京102206
出 处:《陕西电力》2013年第2期36-39,67,共5页Shanxi Electric Power
基 金:国家重点基础研究发展计划资助项目(973项目)(2009CB 724508);北京市自然科学基金资助项目(4122072)
摘 要:局部放电检测和分析是一种公认有效的评估设备绝缘状态的方法。在实验室试验中识别局部放电的类型比较容易,可以参考国家标准和其他文献提供的放电类型的谱图。但现场试验和分析时噪音和干扰的问题仍然是很大的挑战。首先讨论了传统的IEC脉冲电流法和近几年新发展的特高频检测法在检测频率范围上的不足这2种方法由于其有限的带宽,都不能记录整个局部放电电流脉冲的波形,因此缺乏足够的信息来区分不同的信号和噪声。为了解决这个问题,提出了一种基于超宽带在线检测局部放电的方法。该方法涵盖的带宽足够从设备外部记录完整的局部放电脉冲波形采用超带宽检测法,测试了110 kV电缆上不同的放电模式和噪声信号。分析表明,通过完整的波形信息,可以将现场放电分离分类成为不同的放电种类,并可以进一步对这些提纯后的放电模式进行很好的识别。Partial discharge (PD) detection and analysis are widely accepted as an effective method to evaluate the insulation of power apparatus. The detection and diagnosis are relatively easy in a clean laboratory environment with the reference of standard characteristic spectrum available from state regulations and other literatures. But online detection and diagnosis remains a challenge for the noises and interferences around. This article firstly analyzes the bandwidth limitation of traditional pulse current detection method listed in IEC standard,as well as the recent populated Ultra High Frequency (UHF) detection method. Their limited bandwidths related to PD pulses have excluded them from capturing the entire pulse shape. Such lack of PD information has dramatically damaged their ability to differentiate PD pulses from different sources, as well as the noise and interruptions on site. Then a ultra wide band detection method is raised with adequate bandwidth coverage to record the entire PD pulses. From the data analyses of the various signals and noises recorded online from a 110 kV power cable, it is shown that the information extracted from the full size PD pulses can be utilized to separate and classify the PDs front different sources,as well as the noises and interferences. The purified data can be employed to identify the PD sources with prior knowledge available front standards and literatures.
分 类 号:TM835.4[电气工程—高电压与绝缘技术]
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