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Fault diagnosis of electrical power transformer based on water content analysis using Bayesian network

机译:基于贝叶斯网络的含水量分析的电力变压器故障诊断

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摘要

Water content and breakdown voltage of dielectric oil are generally unstable parameters. Exceeding limit permissible threshold of one of parameters implies corrective actions because they are directly related to the oil ability to isolate. In this paper a model based on a Bayesian network (BN) is used to diagnose the causes of transformer failures. The proposed model is used to diagnose the water content in the oil, and to predict the breakdown voltage. A case study of a main transformer (MT) of a power plant is presented to show the effectiveness of our model.
机译:介电油的水含量和击穿电压通常是不稳定的参数。其中一个参数的超出极限允许阈值表示纠正措施,因为它们与油的分离能力直接相关。在本文中,基于贝叶斯网络(BN)的模型用于诊断变压器故障的原因。所提出的模型用于诊断油中的水分含量,并预测击穿电压。提出了一个电厂主变压器(MT)的案例研究,以证明我们模型的有效性。

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