Abstract：Power system frequency dynamic characteristics serve for system stability evaluation and reflects the specific change of frequency when there is a power imbalance between generation and load. On the one hand, the distribution of topology is closely connected with the stability of the power grid in the frequency stability prediction (FSP) problem and the topology of the power grid is typically altered by the random events. On the other hand, the frequency stability characteristics are mirrored in the post-fault response trajectory, so that the time-varying features and dynamic topologies may contribute to the nonlinear spatial-temporal dynamics of FSP. However, the traditional data-driven methods fail to effectively incorporate the system spatial-temporal characteristics into the model training, and suffers from insufficient utilization of system information, poor generalization ability in the face of new topology and interpretability. In addition, the machine learning (ML) model employed for prediction resembles a "black box" internally, and lack of interpretability is one of the primary challenges to ML application in the FSP field. To give a highly accurate FSP reference and denote the potential security hazards of the system, the model is required to identify the major factors that influence the FSP and clarify the decision-making process of model learning. To address these issues, this paper proposes a FSP prediction method that combines the self-attention mechanism (SAM) and the spatial-temporal graph convolutional network (STGCN). Firstly, the proposed STGCN prediction method utilizes a one-dimensional temporal convolutional layer to extract system temporal information. In addition, it employs Chebyshev graph convolution to approximate the Laplacian matrix through polynomial functions, enabling graph convolution operations to capture the topological structure information of each bus and its neighbors. After that, a differentiable self-attention graph pooling (SAGPooling) layer based on SAM is employed to enhance the generalization ability and robustness of the STGCN model. The layer allows the model to reduce the dimensionality of the feature vectors in order to decrease the number of parameters and avoid overfitting. The hierarchical pooling strategy enables the model to preserve valuable node features as much as possible and effectively allocate nodes based on the preserved features and changing topology to enhance the generalization ability and robustness of the STGCN. Meanwhile, the attention scores of each node can be uniformly extracted. Finally, through the SAM, the attention scores of nodes are obtained according to the active power to perform the interpretability analysis of the STGCN model. In summary, this model converts input data into high-level representations of graphics through graph convolution, time convolution, and SAGPooling to integrate the complete spatiotemporal dynamics of FSP. Therefore, the accuracy, generalization ability, and robustness of the proposed STGCN have been improved, and the interpretable analysis of the model decision-making process can be carried out. The testing results on the modified New England 39-bus system and the modified ACTIVSg500 system, which incorporate renewable energy sources, validate the effectiveness of the proposed STGCN. Among all the tested methods, the STGCN has higher prediction accuracy, better robustness, and generalization capability. In addition, the STGCN can provide critical influence factors of different buses on the prediction results in this work.
杜东来, 韩松, 荣娜. 基于时空图卷积网络和自注意机制的频率稳定性预测[J]. 电工技术学报, 2024, 39(16): 4985-4995.
Du Donglai, Han Song, Rong Na. Frequency Stability Prediction Method Based on Modified Spatial Temporal Graph Convolutional Networks and Self-Attention. Transactions of China Electrotechnical Society, 2024, 39(16): 4985-4995.
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2024, Vol. 39 
