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首页> 外文期刊>Dielectrics and Electrical Insulation, IEEE Transactions on >Evaluating the safety condition of porcelain insulators by the time and frequency characteristics of LC based on artificial pollution tests
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Evaluating the safety condition of porcelain insulators by the time and frequency characteristics of LC based on artificial pollution tests

机译:基于人工污染试验,通过LC的时间和频率特性评估瓷绝缘子的安全状况

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摘要

Leakage current (LC) measurement is one of the effective methods for analysis of polluted insulators. But the traditional evaluation methods do not combine well the time-domain with frequency-domain characteristics of LC. In order to evaluate the safety condition of polluted insulators more effectively, an artificial neutral network (ANN) based method was developed in this paper. Firstly, a large number of artificial pollution tests for IEC standard suspension insulators were investigated under different relative humidity (RH) and salt deposit density (SDD). Then, based on the experimental data, the characteristics of LC were analyzed in both time-domain and frequency-domain. The results showed that the peak values of the LC (Ih) has no relation with SDD when the RH is low, but when the RH is high, the Ih increases with the increase of SDD; the phase difference (;8;) between LC and applied voltage decreases with the increase of RH, the LC becomes inductive when strong local arc occurs; the total harmonic distortion (THD) of LC increases slowly with the increase of RH when the pollution is light, but decreases firstly, then increases with the increase of RH when the pollution is middle or heavy level, the value reaches to minimum when the RH is about 80%. Consequently, I>sub>h, ;8; and THD were proposed as characteristic parameters to evaluate the safety condition of polluted insulators. Finally, an ANN with fuzzy output was developed to evaluate the safety condition of polluted insulators. The input parameters of the ANN were Ih, ;8; and THD, while the output parameters were fuzzified into four fuzzy subsets, the capability of ANN was validated by 8 pairs of testing samples, and the effects of SDD and RH on the performance of insulators were discussed.
机译:泄漏电流(LC)测量是分析污染绝缘子的有效方法之一。但是传统的评估方法不能很好地结合LC的时域特性和频域特性。为了更有效地评估被绝缘子污染的安全状况,本文提出了一种基于人工神经网络的方法。首先,在不同的相对湿度(RH)和盐沉积密度(SDD)下,对IEC标准悬挂绝缘子进行了大量的人工污染测试。然后,根据实验数据,分析了LC的时域和频域特性。结果表明,当RH较低时,LC的峰值(Ih)与SDD无关,但是当RH较高时,LC的峰值随SDD的增加而增加。 LC和施加电压之间的相位差(; 8;)随着RH的增加而减小,当发生强烈的局部电弧时LC变为电感性。轻度污染时,LC的总谐波失真(THD)随RH的增加而缓慢增加,但当污染为中度或重度时,其总谐波失真(THD)先降低,然后随RH的增加而增加,RH时该值降至最小值大约是80%因此,I> sub> h,; 8;提出了以THD和THD为特征参数来评价绝缘子污染的安全状况。最后,开发了一种具有模糊输出的人工神经网络,以评估被污染绝缘子的安全状况。 ANN的输入参数为Ih,; 8;在将输出参数模糊化为四个模糊子集的同时,通过8对测试样本验证了人工神经网络的能力,并讨论了SDD和RH对绝缘子性能的影响。

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