| 引用本文: | 石鑫,朱永利,萨初日拉,等.基于深度信念网络的电力变压器故障分类建模[J].电力系统保护与控制,2016,44(1):71-76. |
| SHI Xin,ZHU Yongli,SA Churila,et al.Power transformer fault classifying model based on deep belief network[J].Power System Protection and Control,2016,44(1):71-76 |
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| 摘要: |
| 基于深度信念网络，构建了深度信念网络分类器模型，分析并用典型数据集对其分类性能进行测试。在此基础上结合电力变压器油中溶解气体分析数据，提出了基于深度信念网络分类器的变压器故障分类新方法，它使用油中溶解气体分析结果作为故障分类属性。对所提出的方法进行了测试，测试结果表明该方法适用于变压器故障分类，具有较强的从样本中提取特征的能力和容错特性，性能优于BP神经网络和支持向量机的方法。 |
| 关键词: 电力变压器 故障诊断 深度信念网络 无标签样本 油中溶解气体分析 |
| DOI：10.7667/j.issn.1674-3415.2016.01.010 |
| 分类号: |
| 基金项目:河北省自然科学基金项目(E2009001392) |
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| Power transformer fault classifying model based on deep belief network |
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SHI Xin1, ZHU Yongli1, SA Churila1, WANG Liuwang1, SUN Gang2
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1.School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;2.State Grid Corporation of China, Beijing 100031, China
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| Abstract: |
| Based on deep belief network (DBN), a deep belief network classifier (DBNC) model is built. Analysis and typical data set validate the classification performance of it. Combining power transformer dissolved gas-in-oil analysis (DGA) data, a new transformer fault classification method, which is based on DBNC, is proposed initially. The approach uses the results of DGA as the necessary attributes to classify power transformer’s fault types. The proposed method is tested, which shows that it is suitable for power transformer fault classification. It has strong ability to extract features from samples, and has error-tolerance capability. Test results also show that the performance of the proposed approach prevails that of back propagation neural network (BPNN) and support vector machine (SVM) methods. |
| Key words: power transformer fault diagnosis deep belief network unlabeled samples gas-in-oil analysis |
