基于混合分解多尺度时频图和Res-GRU-AT的电能质量复合扰动识别
能源互联网背景下的电能质量问题越来越凸显,针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种混合分量多尺度时频图和残差神经网络(residual neural network,ResNet)、门控循环单元(gated recurrent units,GRU)网络与注意力机制(attention,AT)组合的电能质量复合扰动识别新方法—Res-GRU-AT.首先利用奇异谱分解(singular spectrum decomposition,SSD)和逐次变分模态分解(succ...
Saved in:
| Published in | 电力系统保护与控制 Vol. 52; no. 4; pp. 12 - 25 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
昆明理工大学电力工程学院,云南 昆明 650500
16.02.2024
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | 能源互联网背景下的电能质量问题越来越凸显,针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种混合分量多尺度时频图和残差神经网络(residual neural network,ResNet)、门控循环单元(gated recurrent units,GRU)网络与注意力机制(attention,AT)组合的电能质量复合扰动识别新方法—Res-GRU-AT.首先利用奇异谱分解(singular spectrum decomposition,SSD)和逐次变分模态分解(successive variational mode decomposition,SVMD)对PQDs信号分别进行多尺度分解得到混合分量,再对混合分量进行希尔伯特黄变换(Hilbert-Huang transform,HHT),分析得到多尺度时频图.其次,利用Res-GRU-AT模型对多尺度时频图进行深层次特征提取、强化和识别.Res-GRU-AT模型能够利用ResNet的二维图像空间特征提取能力和GRU的时序特征提取能力进行特征融合,再通过AT进行特征加权强化,提高了PQDs的识别能力.不同方案的仿真结果表明,所提方法特征提取能力强且抗噪性能好,对复合扰动识别率高. |
|---|---|
| AbstractList | 能源互联网背景下的电能质量问题越来越凸显,针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种混合分量多尺度时频图和残差神经网络(residual neural network,ResNet)、门控循环单元(gated recurrent units,GRU)网络与注意力机制(attention,AT)组合的电能质量复合扰动识别新方法—Res-GRU-AT.首先利用奇异谱分解(singular spectrum decomposition,SSD)和逐次变分模态分解(successive variational mode decomposition,SVMD)对PQDs信号分别进行多尺度分解得到混合分量,再对混合分量进行希尔伯特黄变换(Hilbert-Huang transform,HHT),分析得到多尺度时频图.其次,利用Res-GRU-AT模型对多尺度时频图进行深层次特征提取、强化和识别.Res-GRU-AT模型能够利用ResNet的二维图像空间特征提取能力和GRU的时序特征提取能力进行特征融合,再通过AT进行特征加权强化,提高了PQDs的识别能力.不同方案的仿真结果表明,所提方法特征提取能力强且抗噪性能好,对复合扰动识别率高. |
| Abstract_FL | The power quality problem in the context of the energy internet is becoming more and more prominent.However,there are several problems in the traditional power quality disturbance(PQD)identification process,such as the signal feature extraction is complex,the algorithm recognition ability is insufficient,and it is difficult to differentiate composite disturbance,etc.Thus a new method—Res-GRU-AT,combining hybrid component multi-scale time-frequency diagram,residual neural network(ResNet),gated recurrent units(GRU)network and attention(AT)mechanism,is proposed for power quality composite disturbance identification.First,the PQDs signals are decomposed at multiple scales using singular spectrum decomposition(SSD)and successive variational modal decomposition(SVMD)respectively to obtain the hybrid components.Then the hybrid components are analyzed by Hilbert-Huang transform(HHT)to obtain the multi-scale time-frequency diagram.Secondly,multi-scale time-frequency diagrams are deeply extracted,strengthened,and recognized using the Res-GRU-AT model.The Res-GRU-AT model can perform feature fusion by using the spatial feature extraction capability for 2D images of ResNet and the temporal feature extraction capability of GRU.Then the feature-weighted enhancement is done by AT to improve the recognition capability of PQDs.Simulation results of different schemes show that the proposed method has strong feature extraction capability,good noise immunity,and high recognition rate of composite perturbation. |
| Author | 陈臣鹏 赵四洪 陈仕龙 毕贵红 鲍童语 张梓睿 |
| AuthorAffiliation | 昆明理工大学电力工程学院,云南 昆明 650500 |
| AuthorAffiliation_xml | – name: 昆明理工大学电力工程学院,云南 昆明 650500 |
| Author_FL | BI Guihong CHEN Chenpeng BAO Tongyu CHEN Shilong ZHANG Zirui ZHAO Sihong |
| Author_FL_xml | – sequence: 1 fullname: BI Guihong – sequence: 2 fullname: BAO Tongyu – sequence: 3 fullname: CHEN Chenpeng – sequence: 4 fullname: ZHAO Sihong – sequence: 5 fullname: CHEN Shilong – sequence: 6 fullname: ZHANG Zirui |
| Author_xml | – sequence: 1 fullname: 毕贵红 – sequence: 2 fullname: 鲍童语 – sequence: 3 fullname: 陈臣鹏 – sequence: 4 fullname: 赵四洪 – sequence: 5 fullname: 陈仕龙 – sequence: 6 fullname: 张梓睿 |
| BookMark | eNotj8tKw0AYhWdRwVr7BG7dJs4_M8lklqVoFQpCiesyk4u0SloN4gMotUq9Igh1IWjRggu7UAsBfRqnTd_CgK4OZ_Gdj7OAclErChBaAmyC4A5daZpetNMw23HbMwnFhEEO5cHmzKAMrHlUjOOGwpiCZdmOyKOafkh-kovJeKyvurrbSV-e9KCvR4lOnid3n7PHa33_rW96tSA2KrUto-RO-8fT24_06Ct9H85OLvXgPCMnpyN9NkzfOrr7uojmQrkbB8X_LCB3bdUtrxvVzcpGuVQ1YiGIEQpuSwaBEl5oK48TX9nSsii2MTgKWMilT7LiWIBBYR7wgHGBffA96YNitICW_2YPZRTKaLvebB3sR5mw3vT3SPYcM4wJ_QXBNmx6 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.19783/j.cnki.pspc.230241 |
| DatabaseName | 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 |
| DocumentTitle_FL | Composite PQDs identification based on a hybrid decomposition multi-scale time-frequency map and Res-GRU-AT |
| EndPage | 25 |
| ExternalDocumentID | jdq202404002 |
| GroupedDBID | -03 2B. 4A8 92I 93N ALMA_UNASSIGNED_HOLDINGS CCEZO CEKLB GROUPED_DOAJ PSX TCJ |
| ID | FETCH-LOGICAL-s992-f976a41eb9cf6bc72db6a55306018b14f7ad206085101b07e7e4790d1dcad1b43 |
| ISSN | 1674-3415 |
| IngestDate | Thu May 29 04:03:04 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | 混合模式分解 deep learning 电能质量 故障识别 时频分析 power quality 深度学习 time and frequency analysis,hybrid decomposition fault identification |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s992-f976a41eb9cf6bc72db6a55306018b14f7ad206085101b07e7e4790d1dcad1b43 |
| PageCount | 14 |
| ParticipantIDs | wanfang_journals_jdq202404002 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-02-16 |
| PublicationDateYYYYMMDD | 2024-02-16 |
| PublicationDate_xml | – month: 02 year: 2024 text: 2024-02-16 day: 16 |
| PublicationDecade | 2020 |
| PublicationTitle | 电力系统保护与控制 |
| PublicationTitle_FL | Power System Protection and Control |
| PublicationYear | 2024 |
| Publisher | 昆明理工大学电力工程学院,云南 昆明 650500 |
| Publisher_xml | – name: 昆明理工大学电力工程学院,云南 昆明 650500 |
| SSID | ssib003155689 ssib023166999 ssib002424069 ssj0002912115 ssib051374514 ssib036435463 |
| Score | 2.4330957 |
| Snippet | 能源互联网背景下的电能质量问题越来越凸显,针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种混合分量多尺度时频图和残差神经网络(residual neural... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 12 |
| Title | 基于混合分解多尺度时频图和Res-GRU-AT的电能质量复合扰动识别 |
| URI | https://d.wanfangdata.com.cn/periodical/jdq202404002 |
| Volume | 52 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NaxQxFB9qvXgRRcWvSg_mVKbOZDOT5JjpzloEPZQt9Fbm0y9Yq9teelVqFb8RhHoQtGjBgz2ohQX9a5zt9r_wvUzYDVbwA5bwNpPk_d57m30vIS_jOOcz8OoNCFRdPy-FyxLB3KQIGm5RlsJLwUdQfary8pVwdp5dWggWxg70rVNLK8vpdLb627yS_7Eq1IFdMUv2Hyw7HBQqgAb7QgkWhvKvbEzigMgWiRSJGZYiJnFIIo4ffOQRIZCAUoQkFkRxohpYoxiRConI090DLFWI3SV0B0ISRYn09TgRiWJNUCJm5oque3Fu3lVtEnMcRTBNAIIAeYgGiZpIRIwogQMJTkTLcBVNC1lIhEQECFHpxgCxpbFq0CqyY2eLi24PsKAmAnY1EaEuUBEtIpt6cIUcsUYY1UCpuBk8Gu5J4hNgKwODGjlwrRA6agJAqUYPKoyICgzW0XWW2ERKLZdAkVHR0AlqWqMmAgevJQD4KEGo9aTs3RfK8MB2nRyq54s2S23Dmoi1LjyjKbC2Cox-a_FUUxtzv76GjTmqW0RWY40ebE5nzK-pNj6ILPnUfgRTEHQHnmc5tZAzF6KVwPZ6AbVmN7NcmDnVXgdDdVL6PjeL-4Xaz2adm9enl7pLGSYU0PoKs1_uL7-R30bFobOAUOkgheUctbY-TJiICdiWn8F78YbfYQkShnK0bG5AFG2_xSHwG5wF5jQARmBU4sWFeHp5KLq5cAyBX9gPW2fsdcqkc9UKLttHnMNmVTip6il-1BlbvXbMmave9H70nvR3dqpn69X62uDDu2pzo9ruVb33_Vdf994-r15_r148Gk3I3Y17uy-_DO5-G3ze2rv_tNp8DD37D7arh1uDT2vV-sfjTrsVt2dmXfMKFLeLx8JLWCwkzC9SmZVhmnGap2GCL_oKPV-kPit5klP4ItCzph4veMG49HI_z5LcT1njhDPeudUpTjqTnmABK2iSFzxjzOMp92jJU5ZmEJHmQpxyJowKFs0_XHfRNt3pPzw_4xwazY-zzvjynZViAuL15fScNvZPjWawaw |
| 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%E6%B7%B7%E5%90%88%E5%88%86%E8%A7%A3%E5%A4%9A%E5%B0%BA%E5%BA%A6%E6%97%B6%E9%A2%91%E5%9B%BE%E5%92%8CRes-GRU-AT%E7%9A%84%E7%94%B5%E8%83%BD%E8%B4%A8%E9%87%8F%E5%A4%8D%E5%90%88%E6%89%B0%E5%8A%A8%E8%AF%86%E5%88%AB&rft.jtitle=%E7%94%B5%E5%8A%9B%E7%B3%BB%E7%BB%9F%E4%BF%9D%E6%8A%A4%E4%B8%8E%E6%8E%A7%E5%88%B6&rft.au=%E6%AF%95%E8%B4%B5%E7%BA%A2&rft.au=%E9%B2%8D%E7%AB%A5%E8%AF%AD&rft.au=%E9%99%88%E8%87%A3%E9%B9%8F&rft.au=%E8%B5%B5%E5%9B%9B%E6%B4%AA&rft.date=2024-02-16&rft.pub=%E6%98%86%E6%98%8E%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6%E7%94%B5%E5%8A%9B%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E4%BA%91%E5%8D%97+%E6%98%86%E6%98%8E+650500&rft.issn=1674-3415&rft.volume=52&rft.issue=4&rft.spage=12&rft.epage=25&rft_id=info:doi/10.19783%2Fj.cnki.pspc.230241&rft.externalDocID=jdq202404002 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjdq%2Fjdq.jpg |