| 摘要: |
| 目的 针对变压器油中的 H2 、CH4 、C2 H6 、C2 H4 、C2 H2 气体的浓度存在耦合性问题及电力变压器故障诊断精
度较低的问题,提出了利用堆栈稀疏自编码器( Stacked Sparse Autoencoder, SSAE) 和 XGBoost 模型结合的方法来
提高电力变压器故障诊断的准确率。 方法 首先利用堆栈稀疏自编码器( Stacked Sparse Autoencoder, SSAE) 处理
DGA 数据;其次确定自编码器堆栈个数,确定隐含层数目;然后利用 SSAE 对原始数据进行数据转换,提取深层次
特征信息;接着为了消除数据之间数量级差异较大的问题,对提取后的特征数据归一化进行处理;最后将处理之后
得到的数据再输入 XGBoost 模型之中进行分类验证。 结果 本文建立的基于堆栈稀疏自编码器与 XGBoost 的电力
变压器故障诊断方法诊断准确率为 91. 11%,高于常用的其他机器学习模型。 结论 实验结果验证了方法的有效性,
表明基于堆栈稀疏自编码器与 XGBoost 的电力变压器故障诊断方法能够有效提高故障诊断的准确率。 |
| 关键词: 故障诊断 堆栈稀疏自编码器 特征提取 XGBoost 模型 变压器 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Fault Diagnosis of Power Transformer Based on Stacked Sparse Autoencoder and XGBoost |
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LIANG Haoyu
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School of Electrical and Information Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
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| Abstract: |
| Objective To address the challenge of coupling the concentrations of H2 CH4 C2 H6 C2 H4 and C2 H2 gases
in transformer oil and to improve the accuracy of power transformer fault diagnosis this paper proposed a method using the
combination of Stacked Sparse Autoencoder SSAE and XGBoost model to improve the accuracy of power transformer
fault diagnosis. Methods Firstly a stacked sparse autoencoder SSAE was used to process DGA data. Secondly the
number of stacks in the autoencoder was determined to establish the number of implicit layers. Thirdly SSAE was applied
to transform the original data and extract deep-level feature information. Fourthly to address the issue of significant
magnitude differences between data the extracted feature data were normalized. Finally the processed data was input into
the XGBoost model for classification and verification. Results The fault diagnosis accuracy of the proposed method based
on stacked sparse autoencoder and XGBoost for power transformer was 91. 11% which was higher than that of other
commonly used machine learning models. Conclusion Experimental results verify the effectiveness of the proposed
method demonstrating that this power transformer fault diagnosis method based on stacked sparse autoencoder and
XGBoost can effectively improve the accuracy of fault diagnosis for power transformers. |
| Key words: fault diagnosis stacked sparse autoencoder feature extraction XGBoost model transformer |
