基于物理机理引导的数据驱动潮流计算方法  

Data-driven Power Flow Calculation Method Guided by Physical Mechanism

作　　者：战鹏祥 黄飞虎[1] 廖思睿 彭舰[1] 徐文政 李强 张凌浩 Pengxiang;HUANG Feihu;LIAO Sirui;PENG Jian;XU Wenzheng;LI Qiang;ZHANG Linghao(College of Computer Science,Sichuan University,Chengdu 610065,Sichuan Province,China;State Grid Information&Telecommunication Group Co.,Ltd.,Changping District,Beijing 102211,China;State Grid Sichuan Electric Power Research Institute,Chengdu 610000,Sichuan Province,China)

机构地区：[1]四川大学计算机学院,四川省成都市610065 [2]国网信息通信产业集团有限公司,北京市昌平区102211 [3]国网四川省电力公司电力科学研究院,四川省成都市610000

出　　处：《电网技术》2024年第12期5034-5045,I0051,共13页Power System Technology

基　　金：四川省重点研发计划(2023YFG0112);四川省重点实验室开放课题(SCITLAB-20001);四川大学博士后交叉学科基金项目(10822041A2137)。

摘　　要：随着电力系统可再生能源波动性、负荷随机性等不确定因素的增加,特别是在N-1故障场景下,高效大规模重复潮流计算对于实时安全分析愈发重要。然而,基于物理机理的传统潮流计算方法计算成本较高,运算速度较慢,无法满足实时风险评估需求;数据驱动潮流计算方法运算速度较快,但严重依赖数据质量,预测结果与物理机理缺乏一致性,难以应用于实际工业场景。对此,该文在数据驱动模型上引入电力系统领域知识,构建符合物理约束的深度学习模型,提高了模型性能;采用门控机制和正则化策略,将电力系统拓扑结构和物理公式嵌入到深度神经网络结构,使模型能够适应N-1故障场景下网络拓扑结构的变化。该文采用接入新能源的IEEE 14、IEEE 39以及IEEE 300节点系统进行仿真实验,在正常和N-1故障场景中验证模型效果。实验结果表明,该文方法在误差精度和遵守物理约束的程度上,较传统深度学习潮流计算方法均有提升,可以有效地评估系统在不同故障情况下的运行状态,验证了所提方法的有效性。With the increase of uncertainty factors such as variability of renewable energy and load randomness in power systems,especially in N-1 contingency scenarios,efficient large-scale repetitive power flow calculation is becoming increasingly crucial for real-time security analysis.However,traditional power flow calculation methods based on physical mechanisms have higher computational costs and slower speeds,which can not meet the real-time risk assessment requirements.Data-driven power flow calculation methods have faster speed but rely heavily on data quality,and the prediction results need to be more consistent with physical mechanisms,making it challenging to apply to actual industrial scenarios.To address these issues,this paper introduces power system domain knowledge into data-driven models by constructing a deep learning model that complies with physical constraints,thereby improving the model's performance.It embeds the power system topology structure and physical formulas into the deep neural network structure through a gated mechanism and regularization strategy,enabling the model to adapt to changes in network topology in N-1 contingency scenarios.This paper conducts simulation experiments using the IEEE 14-node and IEEE 39-node systems with new energy access,investigating the model's performance in conventional and N-1 fault scenarios.The experimental results show that the proposed method has improved accuracy and compliance with physical constraints compared to traditional deep learning power flow calculation methods,and can effectively evaluate the system's operating state under different fault conditions,verifying the effectiveness of the proposed method.

关 键 词：潮流计算 数据驱动 深度学习 物理机理引导 拓扑门控 

分 类 号：TM721[电气工程—电力系统及自动化]