摘要：

配电网规划在电力系统中非常重要, 因为它直接影响到电力供应的可靠性、效率和经济性。良好的规划可以确保电力资源得到高效分配, 同时降低运营成本和减少电力损耗。然而, 随着电力需求的增加和系统复杂性的提升, 传统的决策方法不再适用。为提升设备选型、连接配置和电网布局的效率和可靠性, 提出一种基于知识图谱(KG)和图卷积神经网络(GCNN)的配电网智能规划方法KG-GCNN。该方法综合利用KG、图神经网络(GNN)和卷积神经网络(CNN)技术的优势, 为电力系统规划者提供一种智能化的配电网规划方法, 以更好地理解、分析和优化电力系统的设备配置、连接以及物理布局。首先, 建立电力网络的KG, 该KG包含电网的设备、属性及其相互关系, 为后续的分析和优化提供基础; 然后, 利用GNN对电力网络的结构数据进行分析, 以捕捉设备之间的关系和影响, 为设备配置和连接决策提供重要信息; 最后, 引入CNN改善电网的物理布局, 以确定电网中设备的最佳位置和连接方式, 从而提高电网的性能和可靠性。实验结果表明, 通过与决策树、支持向量机(SVM)、循环神经网络(RNN)相比, 该方法能够有效匹配电网中的复杂拓扑结构, 优化电网的物理布局。

关键词: 配电网智能规划, 数据融合, 知识图谱, 图神经网络, 卷积神经网络

Abstract:

Distribution network planning is important in power systems because it directly affects the reliability, efficiency, and economy of power supply. Good planning ensures that power resources are allocated efficiently while reducing operating costs and power losses. However, as power demand and system complexity increase, traditional decision-making methods are no longer applicable. To improve the efficiency and reliability of equipment selection, connection configuration, and grid layout, this study proposes an intelligent distribution network planning method based on Knowledge Graphs (KGs) and Graph Convolutional Neural Networks (GCNNs), i.e., KG-GCNN. This method leverages the advantages of KG, Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs). This study also provides an intelligent distribution network planning method for power system planners to better understand, analyze, and optimize the equipment configuration, connection, and physical layout of power systems. The study first establishes the KG of the power network, which includes the equipment, properties, and interrelationships, and provides the basis for subsequent analysis and optimization. Then, it uses a GNNs to analyze the structural data of the power network to capture the relationship and influence between the devices and to provide important information for equipment configuration and connection decisions. Finally, it introduces CNNs to improve the physical layout of the grid to determine the best location and connection for the devices in the grid, thereby improving its performance and reliability. The experimental results show that, compared with decision trees, Support Vector Machines (SVMs), and Recurrent Neural Networks (RNNs), the proposed method can effectively match the complex topologies of power grids and is suitable for optimizing the physical layout of power grids.

Key words: intelligent planning of distribution network, data fusion, Knowledge Graph (KG), Graph Neural Network (GNN), Convolutional Neural Network (CNN)