基于扩散的生成式机器学习范式用于动态 contingency 分析
A Diffusion-based Generative Machine Learning Paradigm for Dynamic Contingency Screening
AI总结：
本文提出一种基于扩散的生成式机器学习方法，用于动态 contingency 分析，通过预测最可能的critical contingencies来提高实时安全评估效率。
摘要：
动态contingency 分析是动态安全评估中的挑战性任务，当传统数值方法计算成本高且无法实时解决所有可能的contingency 的完整AC潮流时，尤其在大规模电网中更为明显。此外，contingency 造成的严重性并非所有运行点都相同，因此不需要计算所有可能的contingency。本文介绍了一种新颖的基于扩散的生成式机器学习范式，将contingency 分析从传统的场景选择转变为积极的、可能无监督的场景生成。每个运行点到稳态电压稳定极限的边际决定了对应contingency 的排序。通过利用每个运行点的物理信息，所提出的方法预测最可能成为critical的contingency，而无需依赖静态假设或详尽模拟。这种数据驱动的生成方法能够在变化的负荷和发电机条件下识别高风险场景，提供实时动态安全评估。通过方法学推导和对多个IEEE基准系统的综合实验，展示了该方法的正确性、有效性和可扩展性，突显了其在复杂、演进的智能电网中整合contingency 分析的潜力。
英文摘要：
Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies in real time, especially for large-scale power grids. Moreover, the severity caused by a contingency is not identical for all operating points, which does not necessitate solving all possible contingencies computationally inefficient and time-consuming. This paper introduces a novel, diffusion-based generative machine learning paradigm that transforms contingency analysis from conventional scenario selection to a proactive, likely-unsupervised scenario generation. The margin to the steady-state voltage stability limit determines the ranking of contingencies corresponding to each operating point. By leveraging physical information from each operating point, the proposed approach anticipates the contingencies most likely to be critical, without relying on static assumptions or exhaustive simulations. This data-prompted generative approach enables the identification of high-risk scenarios under varying load and generator conditions, providing dynamic security assessment in real time. The correctness, effectiveness, and scalability of the methodology are demonstrated through methodological derivations and comprehensive experiments on multiple IEEE benchmark systems, including IEEE-6, IEEE-14, IEEE-30, and IEEE-118, highlighting its potential to incorporate contingency screening in complex, evolving smart grids.
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