基于DCAE-KSSELM的变压器故障诊断方法

doi:10.11930/j.issn.1004-9649.202111003

中国电力 ›› 2022, Vol. 55 ›› Issue (2): 125-130.DOI: 10.11930/j.issn.1004-9649.202111003

基于DCAE-KSSELM的变压器故障诊断方法

郝玲玲^1,2, 朱永利¹, 王永正³

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

收稿日期:2021-11-01 修回日期:2022-01-14 出版日期:2022-02-28 发布日期:2022-02-23
作者简介:郝玲玲（1995—），女，通信作者，硕士，从事变压器故障研究，E-mail：1061482993@qq.com;朱永利（1963—），男，博士，教授，从事智能信息处理研究，E-mail：yonglipw@163.com;王永正（1993—），男，工程师，从事信息可视化处理研究，E-mail：18330234985@163.com
基金资助:
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)。

Transformer Fault Diagnosis Method Based on DCAE-KSSELM

HAO Lingling^1,2, ZHU Yongli¹, WANG Yongzheng³

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

Received:2021-11-01 Revised:2022-01-14 Online:2022-02-28 Published:2022-02-23
Supported by:
This work is supported by National Natural Science Foundation of China (No.51677072) and Fundamental Research Funds for the Central Universities(No.2018QN078)

摘要/Abstract

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。

关键词: 变压器, 故障诊断, 无标签样本, 收缩自编码器

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 DCAE-KSSELM to make the fault diagnosis. The experimental results show that the proposed hybrid model has good stability, high fault diagnosis accuracy and strong robustness.

Key words: transformer, fault diagnosis, unlabeled sample, contractive autoencoder

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.

HAO Lingling, ZHU Yongli, WANG Yongzheng. Transformer Fault Diagnosis Method Based on DCAE-KSSELM[J]. Electric Power, 2022, 55(2): 125-130.

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https://www.electricpower.com.cn/CN/Y2022/V55/I2/125

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基于DCAE-KSSELM的变压器故障诊断方法

Transformer Fault Diagnosis Method Based on DCAE-KSSELM

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基于DCAE-KSSELM的变压器故障诊断方法

Transformer Fault Diagnosis Method Based on DCAE-KSSELM

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