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首页> 外文期刊>Electric power systems research >Prediction Of Leakage Current Of Non-ceramic Insulators In Early Aging Period
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Prediction Of Leakage Current Of Non-ceramic Insulators In Early Aging Period

机译：老化初期非陶瓷绝缘子漏电流的预测

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The paper presents a neural network based prediction technique for the leakage current (LC) of non-ceramic insulators during salt-fog test. Nearly 50 distribution class silicone rubber (SIR) insulators with three different voltage classes have been tested in a salt-fog chamber, where the LC has been continuously recorded for at least 100 h. A boundary for early aging period is defined by the rate of change of the LC instead of a fixed threshold value. Consequently, the Gaussian radial basis network has been adopted to predict the level of LC at the early stage of aging of the SIR msulators and is compared with a classical network. The initial values of LC and its rate of change at 10 min intervals for the first 5 h are selected as the input to the network, and the final value of LC of the early aging period is considered as the output of the network. It is found that Gaussian radial basis function network with a random optimizing training method is an appropriate network to predict the LC with a 3.5-5.3% accuracy, if the training data and the testing data are selected from the same type of SIR insulators.
机译：本文提出了一种基于神经网络的非陶瓷绝缘子盐雾测试期间漏电流（LC）预测技术。在盐雾室中测试了将近50种具有三种不同电压等级的配电级硅橡胶（SIR）绝缘子，在该室中连续记录了LC至少100小时。早期老化时间的边界由LC的变化率而不是固定的阈值定义。因此，已采用高斯径向基网络来预测SIR模拟器老化早期的LC水平，并将其与经典网络进行比较。选择LC的初始值及其在前5小时内以10分钟为间隔的变化率作为网络的输入，而将早期老化周期的LC的终值视为网络的输出。发现，如果训练数据和测试数据均选自相同类型的SIR绝缘子，则采用随机优化训练方法的高斯径向基函数网络是预测LC精度为3.5-5.3％的合适网络。

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