作　　者： ;

机构地区： 华南理工大学电力学院

出　　处： 《电力工程技术》 2019年第6期16-23,共8页

摘　　要： 为了综合多维度信息,快速准确判断变压器缺陷,同时解决多维度信息融合权重难以确定的问题,文中基于深度学习理论,采用稀疏受限玻尔兹曼机搭建了用于故障诊断的深度学习故障分类模型,结合大型变压器的多维度监测量,提出了一种基于深度置信网络和多维度信息融合的变压器故障诊断方法。该方法能够利用变压器海量的无标签多维监测数据作为学习样本,只需对少量带标签数据进行辅助优化,根据变压器实时在线多维监测数据,被训练后的模型能够对变压器本体状态做出准确的故障诊断。对某市220 kV主变进行诊断测试,结果表明,文中提出方法的故障诊断准确率较现有方法高约4%,验证了该方法的可行性和有效性。 The paper constructs a deep learning fault classification model for fault diagnosis using a Sparse Restricted Boltzmann Machine(Sparse-RBM)based on the deep learning(DP)theory,in order to synthesize multi-dimensional information,determine transformer defects quickly and accurately,and solve the problem that multi-dimensional information fusion weights are difficult to determine.Combined with the multi-dimensional monitoring of large transformers,a transformer fault diagnosis method based on multi-dimensional information fusion and deep believe network is proposed.The method can utilize the massive unlabeled multi-dimensional monitoring data of the transformer as the learning sample,and only needs a small amount of tagged data for auxiliary optimization.The trained model can make an accurate fault diagnosis of the transformer body state according to the real-time online multi-dimensional monitoring data of transformers.The diagnosis test of a 220 kV main transformer in a city is carried out.The test results show that the accuracy of the method proposed in the paper is improved by 4%compared with the existing one.

关 键 词： 电力变压器 多维度信息融合 故障诊断 深度置信网络 稀疏受限玻尔兹曼机

领　　域： []