The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks
Due to the increasing integration of new energy sources, the power system now exhibits low inertia, in which the broadband oscillation problem is increasingly significant in the face of the strong coupling of complex and variable power systems, and the current lack of uniform and effective mathemati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/6062 |
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| _version_ | 1846124238660960256 |
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| author | Jinggeng Gao Yong Yang Honglei Xu Yingzhou Xie Chen Zhou Haiying Dong |
| author_facet | Jinggeng Gao Yong Yang Honglei Xu Yingzhou Xie Chen Zhou Haiying Dong |
| author_sort | Jinggeng Gao |
| collection | DOAJ |
| description | Due to the increasing integration of new energy sources, the power system now exhibits low inertia, in which the broadband oscillation problem is increasingly significant in the face of the strong coupling of complex and variable power systems, and the current lack of uniform and effective mathematical models and analysis methods. To solve this major problem, a broadband oscillation localization method based on the combination of compressed perception and graph attention network (GAT) is proposed. The method firstly uses the principle of compression perception to compress and transmit the oscillation time series data of the sub-station, reconstructs the compressed signal at the master station and aggregates the grid topology and node characteristic information to effectively reduce the redundancy of the oscillation data; reconstruction error is only 0.031, takes into account the balance of the samples and the effectiveness of the computation, and adopts the multi-attention mechanism and the cross-entropy loss function to improve the performance of the model training. Finally, the offline training and online evaluation model based on the GAT algorithm is constructed, and the accuracy of the model is up to 98.5%; and the results show that the method has a high positioning accuracy and a certain anti-noise ability at the same time. |
| format | Article |
| id | doaj-art-bad6a87078234eb49b70b9aa5ef4a287 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-bad6a87078234eb49b70b9aa5ef4a2872024-12-13T16:25:56ZengMDPI AGEnergies1996-10732024-12-011723606210.3390/en17236062The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention NetworksJinggeng Gao0Yong Yang1Honglei Xu2Yingzhou Xie3Chen Zhou4Haiying Dong5State Grid Gansu Electric Power Research Institute, Lanzhou 730000, ChinaState Grid Gansu Electric Power Research Institute, Lanzhou 730000, ChinaState Grid Gansu Electric Power Company, Lanzhou 730000, ChinaState Grid Gansu Electric Power Research Institute, Lanzhou 730000, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaDue to the increasing integration of new energy sources, the power system now exhibits low inertia, in which the broadband oscillation problem is increasingly significant in the face of the strong coupling of complex and variable power systems, and the current lack of uniform and effective mathematical models and analysis methods. To solve this major problem, a broadband oscillation localization method based on the combination of compressed perception and graph attention network (GAT) is proposed. The method firstly uses the principle of compression perception to compress and transmit the oscillation time series data of the sub-station, reconstructs the compressed signal at the master station and aggregates the grid topology and node characteristic information to effectively reduce the redundancy of the oscillation data; reconstruction error is only 0.031, takes into account the balance of the samples and the effectiveness of the computation, and adopts the multi-attention mechanism and the cross-entropy loss function to improve the performance of the model training. Finally, the offline training and online evaluation model based on the GAT algorithm is constructed, and the accuracy of the model is up to 98.5%; and the results show that the method has a high positioning accuracy and a certain anti-noise ability at the same time.https://www.mdpi.com/1996-1073/17/23/6062power systemcompressed sensinggraphical attention networkswideband oscillation positioning |
| spellingShingle | Jinggeng Gao Yong Yang Honglei Xu Yingzhou Xie Chen Zhou Haiying Dong The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks Energies power system compressed sensing graphical attention networks wideband oscillation positioning |
| title | The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks |
| title_full | The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks |
| title_fullStr | The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks |
| title_full_unstemmed | The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks |
| title_short | The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks |
| title_sort | wideband oscillatory localization method based on combining compressed sensing and graph attention networks |
| topic | power system compressed sensing graphical attention networks wideband oscillation positioning |
| url | https://www.mdpi.com/1996-1073/17/23/6062 |
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