基于知识蒸和残余卷积神经网络的变压器故障诊断
Haikun Shang1, Yanlei Wei1, Shen Zhang1
1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
本研究介绍了一种轻量级的卷积神经网络 (CNN),用于使用溶解气体分析 (DGA) 进行变压器故障诊断. 这种新的方法显著降低了计算需求,同时保持了高的诊断准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 溶解气体分析 (DGA) 对于变压器故障诊断至关重要.
- 现有DGA诊断模型的高计算要求带来了挑战.
研究的目的:
- 开发一个轻量级的CNN模型用于变压器DGA诊断.
- 用知识蒸 (KD) 和递归图片来改进气体比率方法.
主要方法:
- 使用比率方法和多尺度样本 (MSE) 的特征提取.
- 对于2D图像特征,使用递归图形进行状态空间重建.
- 使用ResNet50和CBAM进行深度特征提取,由SSA优化.
- 双路径KD将知识从ResNet50教师转移到MobileNetV3-Large学生模型.
主要成果:
- 蒸模型实现了83.5%的内存使用率降低.
- 观察到计算时间减少了73.2%.
- 轻量级模型在评估指标上表现良好.
结论:
- 拟议的轻量级CNN为改进DGA气体比率方法提供了一种新的技术解决方案.
- 在不影响诊断性能的情况下实现了显著的计算复杂性的减少.
- 为实时变压器故障诊断提供了一种有效的方法.
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