Application of Multiattention Mechanism in Power System Branch Parameter Identification
Maintaining accuracy and robustness has always been an unsolved problem in the task of power grid branch parameter identification. Therefore, many researchers have participated in the research of branch parameter identification. The existing methods of power grid branch parameter identification suff...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/1834428 |
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| _version_ | 1832549080609325056 |
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| author | Zhiwei Wang Liguo Weng Min Lu Jun Liu Lingling Pan |
| author_facet | Zhiwei Wang Liguo Weng Min Lu Jun Liu Lingling Pan |
| author_sort | Zhiwei Wang |
| collection | DOAJ |
| description | Maintaining accuracy and robustness has always been an unsolved problem in the task of power grid branch parameter identification. Therefore, many researchers have participated in the research of branch parameter identification. The existing methods of power grid branch parameter identification suffer from two limitations. (1) Traditional methods only use manual experience or instruments to complete parameter identification of single branch characteristics, but they are only used to identify a single target and cannot make full use of the historical information of power grid data. (2) Deep learning methods can complete model training through historical data, but these methods cannot consider the constraints of power grid topological structure, which is equivalent to identifying connected power grid branches separately. To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. Specifically, we input the global features and topology information of branch nodes into our GTN model. In the process of parameter identification, the multihead attention mechanism is used to fuse the branch feature information of different subspaces, which highlights the importance of different branches and enhances the ability of local feature extraction. Finally, the fitting and prediction of each branch feature are completed through the decoding layer. The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions. |
| format | Article |
| id | doaj-art-258908ed24e14139ad5d9a08e9dcf6d3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-258908ed24e14139ad5d9a08e9dcf6d32025-02-03T06:12:10ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/18344281834428Application of Multiattention Mechanism in Power System Branch Parameter IdentificationZhiwei Wang0Liguo Weng1Min Lu2Jun Liu3Lingling Pan4Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaState Grid Zhejiang Electric Power Co., Ltd., Hangzhou, ChinaChina Electric Power Research Institute Co., Ltd., Nanjing 210003, ChinaChina Electric Power Research Institute Co., Ltd., Nanjing 210003, ChinaMaintaining accuracy and robustness has always been an unsolved problem in the task of power grid branch parameter identification. Therefore, many researchers have participated in the research of branch parameter identification. The existing methods of power grid branch parameter identification suffer from two limitations. (1) Traditional methods only use manual experience or instruments to complete parameter identification of single branch characteristics, but they are only used to identify a single target and cannot make full use of the historical information of power grid data. (2) Deep learning methods can complete model training through historical data, but these methods cannot consider the constraints of power grid topological structure, which is equivalent to identifying connected power grid branches separately. To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. Specifically, we input the global features and topology information of branch nodes into our GTN model. In the process of parameter identification, the multihead attention mechanism is used to fuse the branch feature information of different subspaces, which highlights the importance of different branches and enhances the ability of local feature extraction. Finally, the fitting and prediction of each branch feature are completed through the decoding layer. The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions.http://dx.doi.org/10.1155/2021/1834428 |
| spellingShingle | Zhiwei Wang Liguo Weng Min Lu Jun Liu Lingling Pan Application of Multiattention Mechanism in Power System Branch Parameter Identification Complexity |
| title | Application of Multiattention Mechanism in Power System Branch Parameter Identification |
| title_full | Application of Multiattention Mechanism in Power System Branch Parameter Identification |
| title_fullStr | Application of Multiattention Mechanism in Power System Branch Parameter Identification |
| title_full_unstemmed | Application of Multiattention Mechanism in Power System Branch Parameter Identification |
| title_short | Application of Multiattention Mechanism in Power System Branch Parameter Identification |
| title_sort | application of multiattention mechanism in power system branch parameter identification |
| url | http://dx.doi.org/10.1155/2021/1834428 |
| work_keys_str_mv | AT zhiweiwang applicationofmultiattentionmechanisminpowersystembranchparameteridentification AT liguoweng applicationofmultiattentionmechanisminpowersystembranchparameteridentification AT minlu applicationofmultiattentionmechanisminpowersystembranchparameteridentification AT junliu applicationofmultiattentionmechanisminpowersystembranchparameteridentification AT linglingpan applicationofmultiattentionmechanisminpowersystembranchparameteridentification |