电器与能效管理技术Issue(12)：1-8,8.DOI:10.16628/j.cnki.2095-8188.2025.12.001
基于知识蒸馏和增量学习的电能质量扰动分类
Power Quality Disturbance Identification Based on Knowledge Distillation and Incremental Learning
DING Feng 1QIN Chao 2XUE Minjuan 3WU Yiran 4SHI Tianling 4WANG Fei4
作者信息
- 1. College of Transportation,Tongji University,Shanghai 201804,China||National Key Laboratory of Electromagnetic Energy,Shanghai 200030,China||Shanghai Marine Equipment Research Institute,Shanghai 200030,China
- 2. Shanghai Marine Equipment Research Institute,Shanghai 200030,China
- 3. National Key Laboratory of Electromagnetic Energy,Shanghai 200030,China||Shanghai Marine Equipment Research Institute,Shanghai 200030,China
- 4. School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China
- 折叠
摘要
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.关键词
电能质量/增量学习/知识蒸馏/卷积神经网络Key words
power quality/incremental learning/knowledge distillation/convolutional neural network分类
信息技术与安全科学引用本文复制引用
DING Feng,QIN Chao,XUE Minjuan,WU Yiran,SHI Tianling,WANG Fei..基于知识蒸馏和增量学习的电能质量扰动分类[J].电器与能效管理技术,2025,(12):1-8,8.
