A Novel Semisupervised Classification Method for Voltage Sag Based on Virtual Adversarial Mean Teacher Model

Accurate classification of voltage sag is crucial for sag responsibility allocation and optimization of management decisions. Existing voltage sag classification methods rely heavily on massive labeled data during the supervised training process. However, it is difficult to obtain sufficient labeled information from field measurements. To address this issue, this article proposes a novel voltage sag classification method based on semisupervised learning, which is suitable for the situations of limited labeled data. First, voltage sag data is transformed into a two-dimensional graphic form using the space phasor model, enabling automatic feature extraction through convolutional neural networks. Next, a new semisupervised learning algorithm named virtual adversarial mean teacher model (VAMT) is proposed to construct the classifier. The proposed method not only enables the efficient learning of unlabeled data, but also effectively improve the model's robustness against adversarial samples present in both voltage sag dataset and data augmentation process. Finally, based on field-measured dataset, the proposed method exhibits excellent classification performance in comparison with existing methods in complex environments, and is suitable for practical engineering applications.