Abstract:

To accurately and quickly identify power quality disturbances, a convolutional neural network model combining knowledge distillation and incremental learning is proposed. First, a teacher model with high identification accuracy is constructed, and the identification knowledge of the teacher model for the old categories is effectively transferred to the student model through knowledge distillation technology. Then, by improving the traditional knowledge distillation loss function and introducing a dynamic weight mechanism, the student model achieves efficient distillation of old knowledge and enables incremental learning of new knowledge. Compared with the conventional deep learning model, the proposed model adapts to new disturbances without full retraning, which can significantly reduce the training time and saves computing resources while ensuring high identification accuracy.

Key words: power quality, incremental learning, knowledge distillation, convolutional neural network