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

Jinggeng Gao, Yong Yang, Honglei Xu, Yingzhou Xie, Chen Zhou () and Haiying Dong ()
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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

Energies, 2024, vol. 17, issue 23, 1-18

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.

Keywords: power system; compressed sensing; graphical attention networks; wideband oscillation positioning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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