Hybrid data and model‐driven joint identification of distribution‐network topology and parameters
Abstract Because of frequent changes in the topologies of distribution networks, the aging of lines and insufficient monitoring capacity compared to the transmission grid, the topology and line parameters are difficult to determine. Here, a hybrid data and model‐driven method is proposed for identif...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12634 |
| _version_ | 1798023207015415808 |
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| author | Xiu Yang Jiafu Jiang Fang Liu Jinzhang Tang |
| author_facet | Xiu Yang Jiafu Jiang Fang Liu Jinzhang Tang |
| author_sort | Xiu Yang |
| collection | DOAJ |
| description | Abstract Because of frequent changes in the topologies of distribution networks, the aging of lines and insufficient monitoring capacity compared to the transmission grid, the topology and line parameters are difficult to determine. Here, a hybrid data and model‐driven method is proposed for identifying the topologies and line parameters of distribution networks in the absence of voltage‐angle measurements. First, a topology identification model based on an attention mechanism and convolutional neural networks is constructed as an upper‐layer model, which requires a small number of voltage measurement snapshots of key nodes to identify the current topology. Second, a model‐driven two‐stage line‐parameter identification model is constructed as the lower‐layer model. Case studies based on the IEEE33 and PG&E69 node distribution systems are performed to validate the proposed method. The results confirm that the proposed method is effective for a stable and accurate identification of the topology and line parameters of distribution networks within a certain error range. In addition, the proposed method exhibits a reasonable generalization capability, and all models are equally applicable to both radial and ring networks. |
| first_indexed | 2024-04-11T17:42:31Z |
| format | Article |
| id | doaj.art-92d33f17864d4459a4eb408d382574db |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-11T17:42:31Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-92d33f17864d4459a4eb408d382574db2022-12-22T04:11:28ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952022-12-0116234846486610.1049/gtd2.12634Hybrid data and model‐driven joint identification of distribution‐network topology and parametersXiu Yang0Jiafu Jiang1Fang Liu2Jinzhang Tang3College of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaAbstract Because of frequent changes in the topologies of distribution networks, the aging of lines and insufficient monitoring capacity compared to the transmission grid, the topology and line parameters are difficult to determine. Here, a hybrid data and model‐driven method is proposed for identifying the topologies and line parameters of distribution networks in the absence of voltage‐angle measurements. First, a topology identification model based on an attention mechanism and convolutional neural networks is constructed as an upper‐layer model, which requires a small number of voltage measurement snapshots of key nodes to identify the current topology. Second, a model‐driven two‐stage line‐parameter identification model is constructed as the lower‐layer model. Case studies based on the IEEE33 and PG&E69 node distribution systems are performed to validate the proposed method. The results confirm that the proposed method is effective for a stable and accurate identification of the topology and line parameters of distribution networks within a certain error range. In addition, the proposed method exhibits a reasonable generalization capability, and all models are equally applicable to both radial and ring networks.https://doi.org/10.1049/gtd2.12634distribution networksdistribution planning and operationidentificationtopology |
| spellingShingle | Xiu Yang Jiafu Jiang Fang Liu Jinzhang Tang Hybrid data and model‐driven joint identification of distribution‐network topology and parameters IET Generation, Transmission & Distribution distribution networks distribution planning and operation identification topology |
| title | Hybrid data and model‐driven joint identification of distribution‐network topology and parameters |
| title_full | Hybrid data and model‐driven joint identification of distribution‐network topology and parameters |
| title_fullStr | Hybrid data and model‐driven joint identification of distribution‐network topology and parameters |
| title_full_unstemmed | Hybrid data and model‐driven joint identification of distribution‐network topology and parameters |
| title_short | Hybrid data and model‐driven joint identification of distribution‐network topology and parameters |
| title_sort | hybrid data and model driven joint identification of distribution network topology and parameters |
| topic | distribution networks distribution planning and operation identification topology |
| url | https://doi.org/10.1049/gtd2.12634 |
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