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The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks

Author

Listed:
  • Jinggeng Gao

    (State Grid Gansu Electric Power Research Institute, Lanzhou 730000, China)

  • Yong Yang

    (State Grid Gansu Electric Power Research Institute, Lanzhou 730000, China)

  • Honglei Xu

    (State Grid Gansu Electric Power Company, Lanzhou 730000, China)

  • Yingzhou Xie

    (State Grid Gansu Electric Power Research Institute, Lanzhou 730000, China)

  • Chen Zhou

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Haiying Dong

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

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.

Suggested Citation

  • Jinggeng Gao & Yong Yang & Honglei Xu & Yingzhou Xie & Chen Zhou & Haiying Dong, 2024. "The Wideband Oscillatory Localization Method Based on Combining Compressed Sensing and Graph Attention Networks," Energies, MDPI, vol. 17(23), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6062-:d:1534994
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    References listed on IDEAS

    as
    1. Chen, Lei & Xie, Xiaorong & He, Jingbo & Xu, Tao & Xu, Dechao & Ma, Ningning, 2023. "Wideband oscillation monitoring in power systems with high-penetration of renewable energy sources and power electronics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    2. Wu, Huayi & Xu, Zhao, 2024. "Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning," Energy, Elsevier, vol. 303(C).
    3. Hu, Yong & Bu, Siqi & Luo, Jianqiang & Wen, Jiaxin, 2023. "Generalization of oscillation loop and energy flow analysis for investigating various oscillations of renewable energy systems," Renewable Energy, Elsevier, vol. 218(C).
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