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Study on Fault Diagnosis Method of Transformer Using Multi-neural Network and Evidence Theory

机译:多神经网络与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的问题,如故障的不同特征信号来自不同侧的不同特征信号和故障原因和现象的复杂性,呈现了一种使用多神经网络的合成诊断方法和用于变压器故障诊断的证据理论。通过使用神经网络的学习,记忆和识别的优异能力来处理各种数据。整合数据融合方法,神经网络的初步结果是通过证据理论诊断的。通过实验表明,变压器故障诊断的精度率高达73%。

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