• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2015, Vol. 36 ›› Issue (12): 116-122.doi: 10.3969/j.issn.1000-7229.2015.12.018

• 输配电技术 • 上一篇    下一篇

深度学习神经网络在电力变压器故障诊断中的应用

石鑫,朱永利   

  1. 华北电力大学控制与计算机工程学院,河北省保定市 071003
  • 出版日期:2015-12-01
  • 作者简介:石鑫(1988),男,硕士研究生,研究方向为人工智能及应用、变压器故障诊断; 朱永利(1963),男,教授,博士生导师,研究方向为网络化监控与智能信息处理。
  • 基金资助:

    河北省自然科学基金项目(E2009001392)。

Application of Deep Learning Neural Network in Fault Diagnosis of Power Transformer

SHI Xin, ZHU Yongli   

  1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, Hebei Province, China
  • Online:2015-12-01

摘要:

由于电力变压器发生故障时油色谱在线监测数据无标签,工程现场往往会得到大量无标签故障样本,而传统的故障诊断方法在对变压器故障类型进行判别时往往无法充分利用这些无标签故障样本。该文基于深度学习神经网络(deep learning neural network,DLNN),构建了相应的分类模型,分析并用典型数据集对其分类性能进行测试。在此基础上提出一种电力变压器故障诊断新方法,它能够有效利用大量电力变压器油色谱在线监测无标签数据和少量故障电力变压器油中溶解气体分析(dissolved gas-in-oil analysis,DGA)实验数据进行训练,并以概率形式给出故障诊断结果,具有更优的故障判别性能,能够为变压器的检修提供更为准确的参考信息。工程实例测试结果表明,该方法正确可行,诊断性能优于三比值、BP神经网络和支持向量机的方法,适用于电力变压器的故障诊断。

关键词: 电力变压器, 故障诊断, 油中溶解气体分析, 深度学习神经网络

Abstract:

As oil chromatography online-monitoring data is unlabeled during power transformer failure, project sites tend to get a large number of unlabeled fault samples. However, traditional diagnosis methods often fail to make full use of those unlabeled fault samples in judging transformer fault types. Based on deep learning neural network (DLNN), a corresponding classification model was established, whose classification performance was analyzed and tested by typical datasets. On this basis, a new fault diagnosis method of power transformer was further proposed, in which a large number of unlabeled data from oil chromatogram on-line monitoring devices and a small number of labeled data from dissolved gas-in-oil analysis (DGA) were fully used in training process. It could generate fault diagnosis result in the form of probabilities, and provide more accurate information for the maintenance of power transformer because of its better performance in fault diagnosis. Testing results from engineering example indicate that the proposed method is correct and feasible, and its diagnosis performance is better than that of three radio, BP neural network and support vector machine, which is suitable for the fault diagnosis of power transformer.

Key words:  power transformer, fault diagnosis, dissolved gas-in-oil analysis(DGA), deep learning neural network(DLNN)

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