The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label injection (FLI) cyberattacks, resulting in degraded performance of deep TSA models. To address this challenge, a Multi-Module Robust TSA method (MMR) is proposed to rectify the supervised training process misguided by FLI in an unsupervised manner. In MMR, a supervised classification module and an unsupervised clustering module are alternatively trained to improve the clustering friendliness of representation leaning, thereby achieving accurate clustering assignments. Leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and progressively enhance robustness and resilience against FLI. However, there is still a gap on accuracy and convergence speed between MMR and FLI-free deep TSA models. To narrow this gap, we further propose a human-in-the-loop training strategy, named MMR-HIL. In MMR-HIL, potential false samples can be detected by modeling the training loss with a Gaussian distribution. From these samples, the most likely false samples and most ambiguous samples are re-labeled by a TSA experts guided bi-directional annotator and then subjected to penalized optimization, aimed at improving accuracy and convergence speed. Extensive experiments indicate that MMR and MMR-HIL both exhibit powerful robustness against FLI in TSA performance. Moreover, the contaminated labels can also be effectively corrected, demonstrating superior resilience of the proposed methods.

翻译：深度学习在暂态稳定评估（TSA）中的成功高度依赖于高质量的训练数据。然而，TSA数据集中的标签信息容易受到虚假标签注入（FLI）网络攻击的污染，导致深度TSA模型性能下降。为应对这一挑战，提出了一种多模块鲁棒TSA方法（MMR），以无监督方式纠正被FLI误导的监督训练过程。在MMR中，监督分类模块与无监督聚类模块交替训练，以提升表征学习的聚类友好性，从而获得准确的聚类分配。利用聚类分配结果，我们构建了一个训练标签校正器，用于纠正注入的虚假标签，并逐步增强对FLI的鲁棒性和韧性。然而，MMR与无FLI干扰的深度TSA模型在准确率和收敛速度上仍存在差距。为缩小这一差距，我们进一步提出了一种人在回路训练策略，称为MMR-HIL。在MMR-HIL中，通过使用高斯分布对训练损失进行建模，可检测出潜在的虚假样本。从这些样本中，最可能的虚假样本和最模糊样本由TSA专家引导的双向标注器重新标注，随后进行惩罚优化，旨在提升准确率和收敛速度。大量实验表明，MMR和MMR-HIL在TSA性能上均展现出对FLI的强鲁棒性。此外，受污染的标签也能被有效纠正，证明了所提方法的优越韧性。