| 姜学宝,周陈斌,付柳笛,陈 康,王 亮,潘 琪.基于图注意网络和多层感知机的有源配电网拓扑辨识[J].电力需求侧管理,2024,26(5):21-27 |
| 基于图注意网络和多层感知机的有源配电网拓扑辨识 |
| Topology identification of active distribution network based on graph attention network and multi-layer perceptron |
| 投稿时间：2024-05-21 修订日期：2024-07-29 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 004 |
| 中文关键词: 有源配电网 图注意网络 多层感知机 拓扑辨识 多头注意力机制 |
| 英文关键词: active distribution network graph attention network multi- layer perceptron topology identification multi- head attention mechanism |
| 基金项目:国家电网有限公司科技项目（J2023018） |
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| 中文摘要: |
| 拓扑精确感知能更好地支撑新型电力系统薄弱环节评估和负荷调控策略制定。针对有源配电网拓扑结构动态变化难以实时获取的问题,提出一种基于图注意网络和多层感知机的有源配电网拓扑辨识方法。首先,将有源配电网抽象为图模型并用图注意网络自适应学习各节点之间的连接关系,并计算图中各节点多头注意力融合特征；然后,将融合后的节点特征与拓扑中的全部边集合输入至多层感知机中,学习节点融合特征与边连接状态之间的关系,并将图中全部边状态辨识结果整合输出拓扑图级辨识结果；最后,基于IEEE 33节点配电网和IEEE 123节点配电网验证所提方法的有效性,并分析所提方法在不同噪声水平条件下的鲁棒性。同时,将所提模型与其他传统机器学习、深度学习算法进行对比,分析了所提方法的优越性。 |
| 英文摘要: |
| Accurate awareness of power system topology can enhance the assessment of weak links and facilitates development of load regulation strategies. To address the real-time acquisition challenge of dynamic changes in network topology, a topology identification method for distribution networks based on graph attention network（GAT）and multi-layer perceptron（MLP）is proposed. Firstly, the active distribution network is abstracted into a graph model, and the GAT adaptively learnes the relationships between different nodes. Additionally,multi-head attention is employed to calculate the fusion features of each node in the graph. Subsequently, the fused features of nodes and edge sets in the topology are fed into the MLP to learn the relationship between node features and the state of edge connections. The topological graph-level identification results are obtained by integrating all edge states within the network. Finally, the effectiveness of the proposed method is verified in IEEE 33-node and IEEE 123-node distribution networks. The robustness of the proposed method under different noise levels is analyzed. Simultaneously, the proposed model is compared with traditional machine learning and deep learning algorithms to determine its superiority. |
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