| 基于DCAE-KSSELM的变压器故障诊断方法 |
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| 引用本文： | 郝玲玲,朱永利,王永正. 基于DCAE-KSSELM的变压器故障诊断方法[J]. 中国电力, 2022, 55(2): 125-130. DOI: 10.11930/j.issn.1004-9649.202111003 |
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| 作者姓名： | 郝玲玲 朱永利 王永正 |
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| 作者单位： | 1. 华北电力大学 控制与计算机工程学院，河北 保定 071003;2. 国能网信科技（北京）有限公司，北京 100096;3. 中国科学院 计算机网络信息中心，北京 100190 |
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| 基金项目： | 国家自然科学基金资助项目(51677072);中央高校基本科研业务费专项资金资助项目(2018QN078)。 |
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| 摘 要： | 为了充分利用变压器发生故障时产生的大量无标签样本，提高故障诊断精度，提出基于深度收缩自编码器(DCAE)与核半监督极限学习机(KSSELM)相结合的故障诊断方法。首先使用无标签样本对DCAE网络逐层训练，初始化网络参数，然后用有标签样本数据对网络参数进行微调，最后将有标签样本与无标签样本一起作为深度收缩自编码器与核半监督极限学习机(DCAE-KSSELM)混合网络的输入并完成故障诊断。实验结果表明，所提模型稳定性好，故障诊断精度高，鲁棒性强。
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| 关 键 词： | 变压器 故障诊断 无标签样本 收缩自编码器 |
| 收稿时间： | 2021-11-01 |
| 修稿时间： | 2022-01-14 |
Transformer Fault Diagnosis Method Based on DCAE-KSSELM |
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| HAO Lingling,ZHU Yongli,WANG Yongzheng. Transformer Fault Diagnosis Method Based on DCAE-KSSELM[J]. Electric Power, 2022, 55(2): 125-130. DOI: 10.11930/j.issn.1004-9649.202111003 |
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| Authors: | HAO Lingling ZHU Yongli WANG Yongzheng |
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| Affiliation: | 1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;2. Guoneng Wangxin Technology (Beijing) Co., Ltd., Beijing 100096, China;3. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | In order to make full use of the large number of unlabeled samples generated during transformer fault and improve the accuracy of fault diagnosis,an innovative fault diagnosis method is proposed based on the combination of deep contractive autoencoder(DCAE)and kernel semi-supervised extreme learning machines(KSSELM).First,the unlabeled samples are used to train the DCAE network layer by layer and initialize the network parameters.Then the labeled samples are used to fine-tune the network parameters.Finally,the labeled samples and unlabeled samples are used as the inputs of the hybrid network of DCAEKSSELM to make the fault diagnosis.The experimental results show that the proposed hybrid model has good stability,high fault diagnosis accuracy and strong robustness. |
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| Keywords: | transformer fault diagnosis unlabeled sample contractive autoencoder |
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