基于卷积神经网络的变压器故障诊断方法
变压器是电力系统中的重要设备，其安全与稳定直接影响着国民经济的健康发展。油中溶解气体分析（Dissolved Gas Analysis，DGA）是分析变压器故障类别的重要手段。卷积神经网络是深度学习的一种模型，广泛应用于图像识别、语音处理等领域，具有非常好的分类能力。文章选取了变压器的五种油中溶解气体含量作为模型输入量，在借鉴传统浅层BP神经网络油中气体分析方法的基础上，针对BP神经网络表达能力不足以及容易过拟合的缺点，将卷积神经网络应用于变压器故障诊断，并与BP神经网络的分类效果进行了对比，通过算例研究证明了卷积神经网络的效果更优。文章也对卷积神经网络的卷积核个数、卷积核大小以及采样宽度对分...
Saved in:
| Published in | 电测与仪表 Vol. 54; no. 13; pp. 62 - 67 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
华南理工大学 电力学院,广州,510640
2017
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1390 |
Cover
| Abstract | 变压器是电力系统中的重要设备，其安全与稳定直接影响着国民经济的健康发展。油中溶解气体分析（Dissolved Gas Analysis，DGA）是分析变压器故障类别的重要手段。卷积神经网络是深度学习的一种模型，广泛应用于图像识别、语音处理等领域，具有非常好的分类能力。文章选取了变压器的五种油中溶解气体含量作为模型输入量，在借鉴传统浅层BP神经网络油中气体分析方法的基础上，针对BP神经网络表达能力不足以及容易过拟合的缺点，将卷积神经网络应用于变压器故障诊断，并与BP神经网络的分类效果进行了对比，通过算例研究证明了卷积神经网络的效果更优。文章也对卷积神经网络的卷积核个数、卷积核大小以及采样宽度对分类效果的影响进行了探讨。 |
|---|---|
| AbstractList | TM407; 变压器是电力系统中的重要设备,其安全与稳定直接影响着国民经济的健康发展.油中溶解气体分析(Dissolved Gas Analysis, DGA)是分析变压器故障类别的重要手段.卷积神经网络是深度学习的一种模型,广泛应用于图像识别、语音处理等领域,具有非常好的分类能力.文章选取了变压器的五种油中溶解气体含量作为模型输入量,在借鉴传统浅层BP神经网络油中气体分析方法的基础上,针对BP神经网络表达能力不足以及容易过拟合的缺点,将卷积神经网络应用于变压器故障诊断,并与BP神经网络的分类效果进行了对比,通过算例研究证明了卷积神经网络的效果更优.文章也对卷积神经网络的卷积核个数、卷积核大小以及采样宽度对分类效果的影响进行了探讨. 变压器是电力系统中的重要设备，其安全与稳定直接影响着国民经济的健康发展。油中溶解气体分析（Dissolved Gas Analysis，DGA）是分析变压器故障类别的重要手段。卷积神经网络是深度学习的一种模型，广泛应用于图像识别、语音处理等领域，具有非常好的分类能力。文章选取了变压器的五种油中溶解气体含量作为模型输入量，在借鉴传统浅层BP神经网络油中气体分析方法的基础上，针对BP神经网络表达能力不足以及容易过拟合的缺点，将卷积神经网络应用于变压器故障诊断，并与BP神经网络的分类效果进行了对比，通过算例研究证明了卷积神经网络的效果更优。文章也对卷积神经网络的卷积核个数、卷积核大小以及采样宽度对分类效果的影响进行了探讨。 |
| Abstract_FL | Transformer is an important equipment in power system, its security and stability directly affect the healthy development of the national economy.Dissolved gas analysis (DGA) is a key method of transformer fault analysis.The convolutional neural network, as an important model of deep learning, has strong classification ability, which is widely used in image recognition, speech processing, and so on.The content of five kinds of dissolved gases is selected as the input of the model in this paper.On the basic of analysis method of dissolved gases by using BP neural network, according to the shortcomings that BP neural network is insufficient in expression ability and easy to over-fitting, the application of convolutional neural network is proposed to diagnose transformer fault in this paper.Moreover, its simulation proves that the proposed method has a better performance compared with BP neural network.Additionally, the effect of convolution kernel number, kernel size and sampling width of convolutional neural network on the classification results is discussed in this paper. |
| Author | 贾京龙 余涛 吴子杰 程小华 |
| AuthorAffiliation | 华南理工大学电力学院,广州510640 |
| AuthorAffiliation_xml | – name: 华南理工大学 电力学院,广州,510640 |
| Author_FL | Yu Tao Jia Jinglong Wu Zijie Cheng Xiaohua |
| Author_FL_xml | – sequence: 1 fullname: Jia Jinglong – sequence: 2 fullname: Yu Tao – sequence: 3 fullname: Wu Zijie – sequence: 4 fullname: Cheng Xiaohua |
| Author_xml | – sequence: 1 fullname: 贾京龙 余涛 吴子杰 程小华 |
| BookMark | eNotzbtKA0EAheEpIphEX8LCbmEumVspwRsEbNIvO7MzMUFndReR9NooJlgkxBC0Em0khYWY4Nu4O_oWLsTqNB_nr4GKS5ypgCqCEAWISLgOalnWg5BILlEVsPxp8b0Y5Hcf_mXunx_9cui_7v1y5qdX-XCSD27zh9didP07nf3Mb4rxWzH-LN5HG2DNRieZ2fzfOmjv7babB0HraP-wudMKNBVlL8bSKs00ldpojpCSjPGGjDmPqcTGCB0RjoVR1CohVUQYZNZybAS1ljJSB9ur28vI2ch1wl5ykboyGMa631cYIo4IRKiEWyuojxPXOe-W9CztnkZpP2QcM0EpbpA_wK1hOw |
| ClassificationCodes | TM407 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W92 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DatabaseName | 维普_期刊 中文科技期刊数据库-CALIS站点 中文科技期刊数据库-7.0平台 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitleAlternate | Fault diagnosis method of transformer based on convolutional neural network |
| DocumentTitle_FL | Fault diagnosis method of transformer based on convolutional neural network |
| EndPage | 67 |
| ExternalDocumentID | dcyyb201713011 672685524 |
| GrantInformation_xml | – fundername: 国家重点基础研究发展计划(973计划); 国家自然科学基金资助项目 funderid: (2013CB228205); (51477055) |
| GroupedDBID | -03 2B. 2C0 2RA 5XA 5XD 92H 92I 92L ACGFS ALMA_UNASSIGNED_HOLDINGS CCEZO CEKLB CQIGP CW9 TCJ TGT U1G U5M W92 ~WA 4A8 93N ABJNI ADMLS GROUPED_DOAJ PSX |
| ID | FETCH-LOGICAL-c581-1d29fbc6c59cec711b966749d77d592ee8ca3728eb5fb89ba3606ff72e85ff563 |
| ISSN | 1001-1390 |
| IngestDate | Thu May 29 04:07:58 EDT 2025 Wed Feb 14 09:59:42 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 13 |
| Keywords | 变压器 fault diagnosis 卷积神经网络 故障诊断 油中溶解气体分析 convolutional neural network transformer dissolved gas analysis |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c581-1d29fbc6c59cec711b966749d77d592ee8ca3728eb5fb89ba3606ff72e85ff563 |
| Notes | 23-1202/TH transformer, dissolved gas analysis, fault diagnosis, convolutional neural network Jia Jinglong, Yu Tao, Wu Zijie, Cheng Xiaohua （School of Electric Power, South China University of Technology, Guangzhou 510640, China） Transformer is an important equipment in power system, its security and stability directly affect the healthy development of the national economy. Dissolved gas analysis （DGA） is a key method of transformer fault analysis. The convolutional neural network, as an important model of deep learning, has strong classification ability, which is widely used in image recognition, speech processing, and so on. The content of five kinds of dissolved gases is selected as the input of the model in this paper. On the basic of analysis method of dissolved gases by using BP neural net- work, according to the shortcomings that BP neural network is insufficient in expression ability and easy to over-fit- ting, the application of convolutional neural network is proposed to diagnose transformer fault in t |
| PageCount | 6 |
| ParticipantIDs | wanfang_journals_dcyyb201713011 chongqing_primary_672685524 |
| PublicationCentury | 2000 |
| PublicationDate | 2017 |
| PublicationDateYYYYMMDD | 2017-01-01 |
| PublicationDate_xml | – year: 2017 text: 2017 |
| PublicationDecade | 2010 |
| PublicationTitle | 电测与仪表 |
| PublicationTitleAlternate | Electrical Measurement ＆ Instrumentation |
| PublicationTitle_FL | Electrical Measurement & Instrumentation |
| PublicationYear | 2017 |
| Publisher | 华南理工大学 电力学院,广州,510640 |
| Publisher_xml | – name: 华南理工大学 电力学院,广州,510640 |
| SSID | ssj0039791 ssib001129792 ssib051374602 ssib017479537 |
| Score | 2.0887446 |
| Snippet | 变压器是电力系统中的重要设备，其安全与稳定直接影响着国民经济的健康发展。油中溶解气体分析（Dissolved Gas Analysis，DGA）是分析变压器故障类别的重要手段。卷积神经网... TM407; 变压器是电力系统中的重要设备,其安全与稳定直接影响着国民经济的健康发展.油中溶解气体分析(Dissolved Gas Analysis, DGA)是分析变压器故障类别的重要手段.卷积神... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 62 |
| SubjectTerms | 卷积神经网络 变压器 故障诊断 油中溶解气体分析 |
| Title | 基于卷积神经网络的变压器故障诊断方法 |
| URI | http://lib.cqvip.com/qk/92179X/201713/672685524.html https://d.wanfangdata.com.cn/periodical/dcyyb201713011 |
| Volume | 54 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals issn: 1001-1390 databaseCode: DOA dateStart: 20140101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0039791 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR25btRAdBRSQYE4RQhHCoYGLfI1V2nvehVRUC1SupXHR1JtODZFUkMDIhFFIogiqBA0KAUFIhF_w67hL3jv2btrEBKHtLKe38y8c-x5MzvzzNgNJ_VEkuV-S0CA3wqKwLaMU-iWn-S-1rm1JqFdvnfl8r3gzopYmTtxs7FraWNob6dbvz1X8j9eBRz4FU_J_oNnp0QBATD4F67gYbj-lY95LLjp8ijkcYBXHSNGd3ikeKx4CL8uAVAtRiCKuCZM1OHGrTGmjYCB5gE173KjCYi5joiF4SFgJDeAFDw2WBlbaaSvQyqSPOzUQGQQiHxefdhyEvsSF5BTUKkg4nCrSewAJQlDoukCu0lHQEQEhXGtYthG_nBrzKxKQPpUXEGCaFYCwjvYHgAQD2CUEOzjzKooVK5SNHLIPGRCHTcXRKqTn9R5p-U1YMjWQFpLokHmRn4BOqBiHMpbDe0F2sxE0zKyKNjYg_5OJEDBbk3L4G5VeKPJKt1UPX7gDjUIqp3mAFNlyZ48SH5juKgHoirwqD5L8kv27yzd3LSopYtvYgg1IKB0GisHFPVCzKZmf44L11eBdKZZ1PCfW1p3mMiGaUTW1gerDyA4orNqgyIZrDbCqt4ZdrqeDy2FVec-y-a21s6xU40smeeZHL05-nq0PXr-qXx3WL59XR7vlF9elMcH5f7j0c7L0faz0av3490n3_cPvh0-He99GO99Hn_cvcB63bjXXm7Vn_topUKDZJlnCpvKVJg0T5XrWpiJq8BkSmXCeHmu08RXHrw_RGG1sYkPc--iUF6uRVEI6V9k84P1QX6JLTlQR6WZzaVNApEq6yTawsTF-NZYRyULbHFqgP79KqtLXypPaiG8YIFdr03Sr5_1R_2f3XD5jzUW2UmEq7W6K2x--HAjvwrR69BeI9_9ACHhdqM |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8F%98%E5%8E%8B%E5%99%A8%E6%95%85%E9%9A%9C%E8%AF%8A%E6%96%AD%E6%96%B9%E6%B3%95&rft.jtitle=%E7%94%B5%E6%B5%8B%E4%B8%8E%E4%BB%AA%E8%A1%A8&rft.au=%E8%B4%BE%E4%BA%AC%E9%BE%99&rft.au=%E4%BD%99%E6%B6%9B&rft.au=%E5%90%B4%E5%AD%90%E6%9D%B0&rft.au=%E7%A8%8B%E5%B0%8F%E5%8D%8E&rft.date=2017&rft.pub=%E5%8D%8E%E5%8D%97%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6+%E7%94%B5%E5%8A%9B%E5%AD%A6%E9%99%A2%2C%E5%B9%BF%E5%B7%9E%2C510640&rft.issn=1001-1390&rft.volume=54&rft.issue=13&rft.spage=62&rft.epage=67&rft.externalDocID=dcyyb201713011 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F92179X%2F92179X.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fdcyyb%2Fdcyyb.jpg |