摘要
水电调度面临复杂水文条件与动态市场需求的挑战,传统方法难以实现多目标协同优化。提出一种基于自学习知识图谱的智能调度框架,融合图神经网络与强化学习,构建动态感知与自主优化的决策体系。通过知识图谱表征水电系统的拓扑关联与约束规则,结合近端策略优化生成实时调度指令,并利用NSGA-Ⅲ算法实现发电经济性、防洪安全性与设备损耗的多目标权衡。实验模拟某流域梯级水库场景,结果表明:相较于动态规划与规则基准方法,所提方法总发电收益提升10.3%(达750万元),防洪违规次数降为0,设备损耗指数降低41.9%。研究结果表明:自学习知识图谱通过动态编码与闭环反馈机制,显著提升了调度系统的智能化水平与鲁棒性,为应对气候变化与设备老化提供了创新解决方案。未来将探索跨能源协同优化与分布式计算,以增强大规模系统的适应性。
Hydropower scheduling faces challenges from complex hydrological conditions and dynamic market demands,making it difficult for traditional methods to achieve multi-objective optimization.This study proposed an intelligent scheduling framework driven by self-learning knowledge graphs,integrating graph neural networks(GNN)and reinforcement learning(RL)to enable dynamic perception and autonomous decision-making.The framework constructs a knowledge graph to characterize topological relationships and operational constraints of hydropower systems,combines proximal policy optimization(PPO)for real-time scheduling,and employs NSGA-Ⅲto balance economic benefits,flood control safety,and equipment sustainability.Experiments on a cascaded reservoir system demonstrated that the proposed method outperforms dynamic programming and rule-based benchmarks:total power generation revenue increases by 10.3%(reaching 7.5 million yuan),flood control violations are eliminated,and equipment wear index decreases by 41.9%.The results verified that the self-learning knowledge graph enhances system intelligence and robustness through dynamic encoding and closed-loop feedback.This framework provides an innovative solution for addressing climate change and equipment aging.Future work will focus on cross-energy collaborative optimization and distributed computing to improve scalability.
作者
黄帆
涂圣勤
董峰
李宁
胡杨
HUANG Fan;TU Shengqin;DONG Feng;LI Ning;HU Yang(Hubei Qingjiang Hydropower Development Co.,Ltd.,Yichang 443000,China)
出处
《国外电子测量技术》
2025年第4期95-102,共8页
Foreign Electronic Measurement Technology
基金
湖北清江水电开发有限责任公司科研项目(ENQJ-TD1-FW-2024020)。
关键词
水电调度
知识图谱
强化学习
图神经网络
多目标优化
hydropower scheduling
knowledge graph
reinforcement learning
graph neural network
multi-objective optimization
作者简介
黄帆,本科,高级工程师。E-mail:qi1123aa@163.com。
