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机构地区:[1]国网四川省电力公司,四川成都610041 [2]国网四川省电力公司电力科学研究院,四川成都610072
出 处:《四川电力技术》2016年第6期32-35,46,共5页Sichuan Electric Power Technology
摘 要:电力变压器是电力系统中最重要的输变电设备之一,其故障征兆和故障原因之间的关系错综复杂,单项诊断方法信息特征独特、考虑角度单一,通常难以满足其故障诊断要求。提出了一种基于BP神经网络和故障树分析方法的变压器故障综合诊断新模型。首先收集整理变压器故障信息量作为训练和识别样本,建立了基于BP神经网络的变压器故障诊断模型,再利用故障树分析方法,对变压器故障等级、严重程度等进行划分。通过大量的现场数据验证表明,与单一诊断方法相比,该模型能提高故障诊断正确率。研究成果为变压器故障评估诊断提供了一种新思路。Power transformer is one of the most important power transmission equipments in power system, the relationship between the fault symptoms and the fault causes is intricate, and the information characteristics of single diagnosis method is unique when considering from the single point of view, which usually is difficult to meet the needs of fault diagnosis. A new model of transformer fault diagnosis based on BP neural network and fault tree analysis is proposed. Firstly, a collection of transformer fault information is used as training samples to establish the model of transformer fault diagnosis based on BP neural network, and then the fault tree analysis method is adopted to divide the severity and fault level of transformer. Through a large number of field data, the proposed model has improved the diagnosis accuracy compared with the single diagnosis method. The research results provide a new idea for transformer fault diagnosis.
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