生成可导致电力流模型预测误差的对抗样本，揭示神经网络在电网计算中的脆弱性。
Generating adversarial inputs for a graph neural network model of AC power flow
- 通过优化生成使预测与真实解差异最大的输入点
- 最大误差达3.7 pu（无功功率）和0.08 pu（电压幅值）
- 仅需0.04 pu的微小扰动即可触发高误差，适合安全评估与鲁棒训练研究者
本文构建并求解优化问题，生成使神经网络预测的交流潮流解与真实交流潮流方程解之间产生高误差的输入点。以PFΔ基准库实现的CANOS-PF图神经网络模型在14节点测试系统上进行验证。生成的对抗样本导致无功功率误差最高达3.7每单位，电压幅值误差达0.08每单位。当最小化从训练点出发满足对抗约束所需的扰动时，发现仅需对单个母线施加0.04每单位的电压幅值扰动即可满足条件。该工作推动了对交流潮流神经网络代理模型的严格验证与鲁棒训练方法的发展。
原文摘要 · Abstract (English)
This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to the AC power flow equations. We demonstrate this capability on an instance of the CANOS-PF graph neural network model, as implemented by the PF$Δ$ benchmark library, operating on a 14-bus test grid. Generated adversarial points yield errors as large as 3.7 per-unit in reactive power and 0.08 per-unit in voltage magnitude. When minimizing the perturbation from a training point necessary to satisfy adversarial constraints, we find that the constraints can be met with as little as an 0.04 per-unit perturbation in voltage magnitude on a single bus. This work motivates the development of rigorous verification and robust training methods for neural network surrogate models of AC power flow.
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