南方电网技术2025，Vol.19Issue(7)：3-14,12.DOI:10.13648/j.cnki.issn1674-0629.2025.07.001
基于图卷积网络的新型电力系统实时无功功率优化
Real-Time Reactive Power Optimization for New Power Systems Based on Graph Convolutional Networks
摘要
Abstract
The rapid fluctuations in renewable energy output cause frequent voltage oscillations in power grids,severely threatening the safe and economical operation of power grids.To address this issue,a real-time reactive power optimization method based on graph convolutional networks(GCN)is proposed.Firstly,a multi-objective reactive power optimization model considering the importance of nodes is constructed.Based on this,the graph representation of the reactive power optimization model is established,and the adjacency matrix is restructured in conjunction with the optimization problem.Then,the optimal solution set is mapped using the GCN algorithm,and the improved CRITIC-AHP-TOPSIS combined weighting algorithm is employed to select the optimal solu-tion,forming a real-time optimization strategy.Finally,using the modified IEEE 39-node system and an actual power grid as examples,the proposed method is verified.The results show that the method not only has the advantages of fast solving speed and avoiding local optima but also achieves more favorable voltage deviation and active power loss,ensuring the safety and economy of new power system operation.关键词
图卷积网络/新能源/实时无功功率优化/节点重要性/最优解Key words
graph convolutional networks/renewable energy/real-time reactive power optimization/importance of nodes/optimal solution分类
信息技术与安全科学引用本文复制引用
张沛,刘晓菲,李文云,路学刚,王珍意,翟苏巍,孙慧博..基于图卷积网络的新型电力系统实时无功功率优化[J].南方电网技术,2025,19(7):3-14,12.基金项目
中国南方电网有限责任公司云南电力调度控制中心科技项目(YNKJXM20222463).Supported by the Science and Technology Project of Yunnan Electric Power Dispatching and Control Center,China Southern Power Grid Co.,Ltd.(YNKJXM20222463). （YNKJXM20222463）
