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Prediction of Breakdown Strength of Non-cellulosic Insulating Materials using Artificial Neural Networks

机译:人工神经网络预测非纤维素绝缘材料的击穿强度

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In this research work, two sets of experiments are performed in the high voltage laboratory to measure different electrical parameters of insulating materials like breakdown strength, relative permittivity, loss tangent, etc. for a few non-cellulosic materials like teflon, nomex, laminated nomex, glass bonded mica, epoxy resin bonded mica paper, and epoxy resin bonded fiber glass. Also, Artificial Neural Networks (ANNs) models are proposed for the prediction of breakdown strength, considering its dependency on other electrical parameters like relative permittivity, loss tangent, volume resistivity, and thickness of material. The ANN model results are compared with those obtained experimentally and also with the values already predicted from an empirical relation suggested by J. W. Swanson and F.C. Dall. The reported results indicated that the breakdown strength predicted from the ANN model is in good agreement with the experimental values.
机译:在这项研究工作中,在高压实验室中进行了两组实验,以测量绝缘材料的不同电参数,例如击穿强度,相对介电常数,损耗角正切等。对于一些非纤维素材料,例如聚四氟乙烯,nomex,层压nomex ,玻璃粘合云母,环氧树脂粘合云母纸和环氧树脂粘合纤维玻璃。此外,考虑到它对其他电参数(如相对介电常数,损耗角正切,体积电阻率和材料厚度)的依赖性,提出了人工神经网络(ANN)模型来预测击穿强度。将ANN模型的结果与通过实验获得的结果进行比较,并与根据J.W. Swanson和F.C.达尔报道的结果表明,由ANN模型预测的击穿强度与实验值非常吻合。

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