基于多尺度混卷积神经网络的三级中性点紧逆变器的开放电路故障诊断方法
Yan Yan1, Jiaqi Wu1, Yanfei Cao1
1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了用于逆变器中功率切换装置的新型故障诊断模型. 多尺度混合卷积神经网络 (MSSCNN) 为可靠的逆变器操作提供了更好的准确性和抗噪能力.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 三级中性点紧式 (3L-NPC) 逆变器在功率电子应用中至关重要.
- 电源开关设备的开放电路故障可能导致系统故障和损坏.
- 准确和高效的故障诊断对于可靠的逆变器运行至关重要.
研究的目的:
- 开发一个先进的故障诊断模型,用于3L-NPC逆变器的开放电路故障.
- 通过使用深度学习技术来提高诊断准确度和降低模型复杂性.
- 评估拟议模型的性能与现有的深度学习架构相比.
主要方法:
- 基于多尺度混合卷积神经网络 (MSSCNN) 的故障诊断模型的构建.
- 在MSSCNN架构中使用深度可分离的卷积和道混合技术.
- 从逆变器输出电流信号中提取和分类故障信息.
主要成果:
- 与CNN,ResNet,ShuffleNet V2和Mobilenet V3相比,MSSCNN模型的复杂性较低.
- 拟议的模型表现出优越的抗噪性能.
- 通过MSSCNN模型实现了显著更高的平均诊断准确度.
结论:
- 基于MSSCNN的故障诊断模型为3L-NPC逆变器的开放电路故障提供了一个有希望的解决方案.
- 该模型的效率和准确性使其适用于现实世界的应用.
- 这项研究强调了深度可分离的卷积和通道混合的有效性,以改善故障诊断.
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