使用单个深度神经网络进行交流潮流事故分析
AC Power Flow Contingency Analysis Using a Single Deep Neural Network
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中文总结 AI 辅助
提出复用单个基于基态数据训练的深度神经网络，通过不动点迭代预测任意单线路停运后的电网状态，并给出收敛条件及SDP验证方法，在IEEE 118节点系统上验证了准确性与高效性。
中文摘要 AI 辅助
基于交流潮流（AC-PF）模型的事故分析是准确评估电网安全性的关键工具，但其计算负担随着需评估的运行场景和停运配置数量的增加而增大。近期基于机器学习的方法通常需要针对特定事故的训练数据，导致离线训练成本随事故数量增加而扩展。本文提出一种框架，复用仅基于基态交流潮流数据训练的单个机器学习模型，来估计任意单线路停运下的事后运行状态。所提方法将事后状态预测表述为不动点迭代。若机器学习模型为深度神经网络（DNN），我们推导出保证收敛的充分条件，并开发半定规划（SDP）公式以针对给定DNN验证这些条件。在IEEE 118节点系统上的数值测试表明，所提出的SDP公式是紧的，验证条件对所有测试的事故均成立，且所提方法仅需几次迭代即可产生准确的事后状态估计。
英文摘要
Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden increases with the number of operating scenarios and outage configurations to evaluate. Recent ML-based approaches typically require outage-specific training data, leading to offline training costs that scale with the number of contingencies. This work proposes a framework that reuses a single ML model trained solely on basecase AC-PF data to estimate post-contingency operating states under arbitrary single-line outages. The proposed approach formulates post-contingency state prediction as a fixed-point iteration. If the ML model is a deep neural network (DNN), we derive sufficient conditions that guarantee convergence and develop semidefinite programming (SDP) formulations to certify these conditions for a given DNN. Numerical tests on the IEEE 118-bus system demonstrate that the proposed SDP formulations are tight, that the certified conditions hold for all tested contingencies, and that the resulting method produces accurate post-contingency state estimates within only a few iterations.
