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Study on fault diagnosis method of transformer using multi-neural network and evidence theory

机译:基于神经网络和证据理论的变压器故障诊断方法研究

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摘要

In order to solve the problems of power transformer such as the fault can be reflected by different characteristic signal from different side and complexity of fault reason and phenomenon, a synthetic diagnosis method using multi-neural network and evidence theory for transformer fault diagnosis is presented. Various kinds of data are dealt by using neural network's excellent abilities of learning, memory and recognition. Integrating data fusion methods, neural network's preliminary results are diagnosed by evidence theory. It has been shown by experiments that the accuracy rate of transformer fault diagnosis is up to 73%.
机译:为了解决电力变压器故障的问题,可以通过不同侧的不同特征信号反映故障,并提出故障原因和现象的复杂性,提出了一种基于多神经网络和证据理论的变压器故障诊断综合诊断方法。利用神经网络出色的学习,记忆和识别能力,可以处理各种数据。结合数据融合方法,通过证据理论对神经网络的初步结果进行诊断。实验表明,变压器故障诊断的准确率高达73%。

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