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Diagnostic Measurement in GIS based on Frequency Analysis of Partial Discharge in SF_6 Gas

基于SF_6气体局部放电频率分析的GIS诊断测量

基本信息

批准号：
62550206
负责人：
ARAI Kenji
金额：
$ 1.09万
依托单位：
Faculty of Engineering, Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1987
资助国家：
日本
起止时间：
1987 至 1988
项目状态：
已结题

项目摘要

To investigate the fundamental properties about the detection of abnormal discharge in GIS, short-gapdischarge phenomena were studied in air and SF_6 gas. The results can be summarized as follows:(1) In the spark-type discharge, pulse width of the current wave shape is several nano seconds, and the peak value of it is several amperes. In this case, the radiated electromagnetic wave, of which frequency spectrum extends to more wider than 1 GHz, is strong enough to be detected with an antenna. in the corona-type discharge, the peak value of the current wave shape is smaller than several milli amperes, and the level of its radiated electromagnetic wave is comparable to that of the background noise. Therefore, it is more difficult to detect the corona-type discharge than the spark-type short gap discharge.(2) Current wave shape of the corona-type discharge in SF_6 gas is remarkably different from that in air. The wave tail is about 2 nano seconds in SF_6 gas, and the one in air is several ten nano seconds. To clear the cause of these differences, a calculation of a single electron avalanche model was carried out.In SF_6 gas, negative ion cloude of high density is produced very near the tip of the negative needle, in a time shorter than 1 nano second after the generation of trigger electron. In air, the negative ion cloud of low density is produced at a distance from the tip of the negative needle. Therefore, it can be saide that suppression of the discharge requires repetition of many avalanche developments in air, and as the result the wave tail of the discharge current is longer.(3) The digital processing of the frequency spectrum constitutes an effective way to detect the corona-type discharge. In this way it is possible to detect a positive corona discharge of several ten pico Coulombs. On the other hand, detection of a negative corona discharge is difficult even with digital processing, due to small charge pulse, and more studies are required.

为了研究GIS中异常放电检测的基本特性，对空气和SF_6气体中的短间隙放电现象进行了研究。结果可概括如下： (1)火花式放电中，电流波形的脉冲宽度为几纳秒，峰值为几安培。在这种情况下，频谱延伸到比1GHz更宽的辐射电磁波足够强，可以用天线检测到。在电晕型放电中，电流波形的峰值小于几毫安，其辐射电磁波的水平与背景噪声的水平相当。因此，电晕型放电比火花型短间隙放电更难检测。(2)SF_6气体中电晕型放电的电流波形与空气中明显不同。 SF_6气体中的波尾约为2纳秒，空气中的波尾为数十纳秒。为了弄清楚造成这些差异的原因，进行了单电子雪崩模型的计算。在SF_6气体中，在触发电子产生后不到1纳秒的时间内，在负针尖端附近产生了高密度的负离子云。在空气中，在距负针尖端一定距离处产生低密度的负离子云。因此，可以说，抑制放电需要在空气中重复多次雪崩发展，从而导致放电电流的波尾较长。(3)频谱的数字处理是检测电晕型放电的有效方法。通过这种方式，可以检测几十皮库仑的正电晕放电。另一方面，由于电荷脉冲很小，即使采用数字处理，负电晕放电的检测也很困难，需要更多的研究。