  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.
