Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers significant advantages over shallow learning methods and other deep learning-based approaches.

翻译：深度学习已成为应对电力系统短时电压稳定性评估（STVSA）挑战的有效解决方案。然而，现有基于深度学习的STVSA方法在适应拓扑变化、样本标注和小数据集处理方面存在局限性。为克服这些挑战，本文提出一种基于相量测量单元（PMU）量测的新型短时电压稳定性评估方法，该方法采用深度迁移学习技术。该方法利用PMU捕获的实时动态信息构建初始数据集，通过时序集成进行样本标注，并采用最小二乘生成对抗网络（LSGAN）进行数据增强，从而实现在小规模数据集上的有效深度学习。此外，该方法通过探索不同故障间的关联性，增强了对拓扑变化的适应能力。在IEEE 39节点测试系统上的实验结果表明，所提方法通过迁移学习使模型评估精度提升约20%，并展现出对拓扑变化的强适应性。借助Transformer模型的自注意力机制，该方法在浅层学习方法及其他深度学习方法中展现出显著优势。