基于深度学习的变压器故障诊断方法研究  

Diagnosis methodof power transformer fault based on deep learning

作　　者：杨涛 黄军凯 许逵 吴建蓉 陈仕军 YANG Tao;HUANG Junkai;XU Kui;WU Jianrong;CHEN Shijun(Electric Science Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002 Guizhou,China)

机构地区：[1]贵州电网有限责任公司电力科学研究院,贵州贵阳550002

出　　处：《电力大数据》2018年第6期23-30,共8页Power Systems and Big Data

摘　　要：油中溶解气体分析方法(DGA)是变压器内部故障诊断的重要方法,广泛应用在变压器在线监测和定期试验检测中,传统的特征气体法和三比值法等诊断方法在实际应用中普遍存在着一定的局限性,导致故障诊断精度偏低。针对这一问题,本文提出了一种基于深度学习技术中的多层感知机的变压器故障综合诊断方法,利用开源的Scikit-learn机器学习框架及Tensor Flow深度学习框架构建了变压器故障诊断模型,并应用实际工程中的故障样本数据,对故障诊断模型进行了训练和测试。试验结果表明,基于多层感知机技术的变压器故障诊断模型能够对变压器故障进行正确诊断,与传统的三比值法及支持向量机技术相比,多层感知机的诊断准确率更高,具有更优的故障诊断性能,能够为变压器的检修提供更为准确的参考信息。The dissolved gas analysis method（ DGA） in oil is an important method for internal fault diagnosis of transformers. It is widely used in transformer on-line monitoring and periodical test detection. Traditional diagnostic methods such as characteristic gas method and three-ratio method are commonly used in practical applications. These methods have some limitations which caused the fault diagnosis accuracy to be low. In order to solve this problem,this paper proposes a transformer fault diagnosis method based on multilayer perceptron in deep learning technology. It uses the open source Scikit-learn machine learning framework and Tensor Flow deep learning framework to construct a transformer fault diagnosis model. The fault diagnosis model was trained and tested by using the fault sample data in practical engineering. The test results show that the transformer fault diagnosis model based on multi-layer perceptron technology can correctly diagnose the transformer fault. Compared with the traditional three-ratio method and the support vector machine techniques,the multi-layer perceptron has a higher diagnostic accuracy and better fault diagnosis performance,which can provide more accurate reference information for transformer overhaul.

关 键 词：深度学习 油中气体分析 变压器 故障诊断 多层感知机 

分 类 号：TM42[电气工程—电器]