基于物理信息神经网络的电网连锁故障风险评估

武延林; 卢健强; 朱禹泓; 周永智

武延林,卢健强,朱禹泓,周永智.基于物理信息神经网络的电网连锁故障风险评估[J].电测与仪表,2025,62(12):160-167.
Wu Yanlin,Lu Jianqiang,Zhu Yuhong,ZHOU Yongzhi.Risk Assessment of Cascading Failure in Power Systems Based on Physics-informed Neural Network[J].Electrical Measurement & Instrumentation,2025,62(12):160-167.

基于物理信息神经网络的电网连锁故障风险评估

Risk Assessment of Cascading Failure in Power Systems Based on Physics-informed Neural Network

DOI：

中文关键词: 连锁故障风险评估物理信息神经网络数据驱动故障链

英文关键词:cascading failure risk assessment physics-informed neural network data driven fault chain.

基金项目:国家电网公司科技资助项目（52660422000A）

作者中文名	作者英文名	单位
武延林	Wu Yanlin	国网内蒙古东部电力有限公司电力科学研究院
卢健强	Lu Jianqiang	国网内蒙古东部电力有限公司电力科学研究院
朱禹泓	Zhu Yuhong	浙江大学工程师学院
周永智	ZHOU Yongzhi	浙江大学工程师学院

摘要点击次数: 8

中文摘要:

随着新能源的高比例接入，电网连锁故障风险评估对于电网的安全稳定运行日益重要，新能源的随机波动性和电力电子并网特性使得连锁故障将呈现更强的随机性和复杂性。传统连锁故障模型无法有效反映新能源并网单元因高低电压穿越引起的脱网，且大规模电网的风险评估依然面临准确性与复杂度的矛盾。针对此问题，本文提出了一种基于物理信息神经网络的连锁故障风险评估方法。首先，重点考虑电压穿越特性构建了新能源随机脱网和线路过载跳闸的不确定性模型，同时引入故障图链描述连锁故障过程中电气量状态、拓扑关系等物理特征的状态转移特性。其次，提出了故障图对应的连锁故障风险指标，并结合数据驱动通过经验回放计算，并构建融合物理信息的神经网络，通过物理信息神经网络将电力系统连锁故障过程的关键物理特征嵌入数据驱动模型，拟合故障图与连锁故障风险指标之间的非线性映射关系，进而实现风险快速评估。最后，通过算例分析验证了所提方法的有效性。

英文摘要:

With the increasing integration of new energy sources, the risk assessment of cascading faults in the power grid has become increasingly crucial for its safe and stable operation. The stochastic variability of new energy sources and the characteristics of power electronics integration contribute to a heightened level of randomness and complexity in cascading faults. Traditional cascading fault models fail to effectively capture the disconnection of new energy integration units due to voltage fluctuations, and the risk assessment for large-scale power grids still grapples with the trade-off between accuracy and complexity. To address this challenge, this paper proposes a cascading fault risk assessment method based on a physical information neural network. Firstly, we establish uncertainty models for the random disconnection of new energy sources and line overloads. Simultaneously, we introduce a fault graph chain to describe the state transition characteristics of electrical quantities, topological relationships, and other physical features during cascading fault processes. Secondly, we present cascading fault risk indicators corresponding to the fault graph. Through data-driven empirical replay calculation, we integrate physical information into a neural network, aiming to approximate the nonlinear mapping relationship between fault graphs and cascading fault risk indicators, facilitating rapid risk assessment. Finally, the effectiveness of the proposed method is validated through case studies.

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