用可证明可靠的神经网络实现电网故障快速筛查，零误判且提速十倍以上。
Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks
- 用输入凸神经网络构建可验证可靠性的故障筛查模型
- 在IEEE 39节点系统上实现10-20倍加速，准确率高且零漏检
- 适合电力系统安全分析人员，尤其关注可靠性与效率的场景
电力系统运行需确保在元件同时故障（即 $N-k$ 故障）时调度方案仍可行，以防连锁崩溃。但全面检查所有 $N-k$ 可能性在计算上不可行，当前依赖启发式筛选方法，可能遗漏关键故障组合，导致误判为安全的危险情形（假阴性）。本文提出使用输入凸神经网络（ICNN）进行故障筛查。我们证明，通过求解一个凸优化问题可确定ICNN的可靠性，并将该优化作为可微层嵌入训练过程，使模型兼具数据驱动与可证明可靠性。结果是：该方法可保证零假阴性率。在IEEE 39节点测试系统上的实证表明，该方法实现10-20倍的速度提升，同时保持高分类准确率。
原文摘要 · Abstract (English)
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.
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