标题： 快速可靠的输入凸神经网络$N-k$切入筛选 显示英文标题

标题： Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks

摘要： 电力系统运营商必须确保调度决策在电网故障或紧急情况发生时仍然可行，以防止连锁故障并确保可靠运行。然而，检查所有$N - k$种紧急情况——即$k$个电网组件同时失效的每一种可能性——即使是对于小型$k$来说，也是计算上不可行的，这要求系统运营商求助于启发式筛选方法。 由于不确定性的增加和系统行为的变化，启发式列表可能并不包括所有相关的紧急情况，在这种情况下，不安全的情景可能会被错误地分类为安全的，从而产生假阴性结果。 在这项工作中，我们提议使用输入凸神经网络（ICNNs）来进行紧急情况筛查。我们表明，可以通过解决一个凸优化问题来确定ICNN的可靠性，并通过在训练期间使用该问题作为可微优化层来调整模型权重，我们可以学习到一个既具有数据驱动特性又有可靠保证的ICNN分类器。也就是说，我们的方法可以确保零假阴性率。 我们在IEEE 39节点测试网络上的案例研究中实证验证了这种方法，观察到它能够实现显著的速度提升（10-20倍），同时具有出色的分类准确性。 显示英文摘要

摘要： Power system operators must ensure that dispatch decisions remain feasible in case of grid outages or contingencies to prevent cascading failures and ensure reliable operation. However, checking the feasibility of all $N - k$ contingencies -- every possible simultaneous failure of $k$ grid components -- is computationally intractable for even small $k$, requiring system operators to resort to heuristic screening methods. Because of the increase in uncertainty and changes in system behaviors, heuristic lists might not include all relevant contingencies, generating false negatives in which unsafe scenarios are misclassified as safe. In this work, we propose to use input-convex neural networks (ICNNs) for contingency screening. We show that ICNN reliability can be determined by solving a convex optimization problem, and by scaling model weights using this problem as a differentiable optimization layer during training, we can learn an ICNN classifier that is both data-driven and has provably guaranteed reliability. Namely, our method can ensure a zero false negative rate. We empirically validate this methodology in a case study on the IEEE 39-bus test network, observing that it yields substantial (10-20x) speedups while having excellent classification accuracy.