基于多头注意力机制改进图神经网络的新能源电力系统风险评估  

Risk Assessment of Renewable Energy Power Systems via Graph Multi-Attention Networks

作　　者：白云鹏 张志艳 许才 郭创新[2] 刘祝平 朱文昊 BAI Yunpeng;ZHANG Zhiyan;XU Cai;GUO Chuangxin;LIU Zhuping;ZHU Wenhao(Electric Power Research Institute,State Grid East lnner Mongolia Electric Power Co.,Ltd.,Hohhot 010020,China;Department of Electrical Engineering,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]国网内蒙古东部电力有限公司电力科学研究院,呼和浩特市010020 [2]浙江大学电气工程学院,杭州市310027

出　　处：《电力建设》2025年第1期147-157,共11页Electric Power Construction

基　　金：国网蒙东电力公司科技项目(526604230006)。

摘　　要：随着全球能源转型和可再生能源的快速发展,新能源电力系统面临着新的机遇挑战与转型升级。这种转型引发电网有关“安全与稳定”命题的新挑战,如风电和光伏等新能源的大规模接入带来的频率越限、电压失稳等新问题。文章综合考虑新能源出力与天气因素影响下的设备故障,建立了新能源接入情况下的电网综合场景;结合图神经网络与多头注意力机制提出了多头图注意力神经网络模型,通过并行训练方法将所提模型应用于新能源电力系统风险评估场景,旨在兼顾电网风险评估准确性的前提下,提高评估效率;应用某省电网的实际数据,在电力仿真系统上进行训练与测试。结果显示,相较于其他人工智能方法在风险评估中的应用,基于多头注意力机制的图神经网络风险评估方法能够提升新能源电力系统风险评估的鲁棒性与高效性,具有一定的应用价值。The accelerating global energy transition and rapid expansion of renewable energy sources,presents both opportunities and challenges.This transformation has introduced new concerns related to the“safety and stability”of power grids,particularly as large-scale integration of renewable energy sources such as wind and solar power results in issues including frequency overruns and voltage instability.This study explores the impact of renewable energy output and weather conditions on equipment failures and establishes a comprehensive scenario for power grids under renewable energy integration.A novel multihead graph-attention neural network model is proposed that integrates graph neural networks with multihead attention mechanisms.By incorporating parallel training methods,the proposed model is utilized in renewable energy power systems with the aim of improving risk assessment efficiency while maintaining accuracy in grid risk assessments.The model is trained and tested using data obtained from a provincial power grid within an electrical power simulation system.Results,derived from integrating real-world data from a provincial power grid in China with that of the electrical power simulation system,demonstrate that the attention-based graph neural network method approach substantially improves the robustness and efficiency of risk assessments compared to other artificial intelligence methods.This approach shows considerable promise in renewable energy power systems for enhancing risk assessment.

关 键 词：新能源电力系统 深度学习 注意力机制 风险评估 风险分析 

分 类 号：TM73[电气工程—电力系统及自动化]