基于卷积神经网络的变压器故障诊断使用振动信号
Chao Li1, Jie Chen1, Cheng Yang2
1School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的深度学习方法,用于使用振动分析来诊断干型变压器故障. 这种新的方法实现了99%以上的准确性,提高了变压器的安全性和可靠性.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 变压器的安全性和成本效益取决于快速准确的故障诊断.
- 振动分析是一种有希望的,低成本的方法,用于变压器故障诊断,但面临复杂的操作条件的挑战.
- 干式变压器由于其独特的操作环境,需要专门的诊断技术.
研究的目的:
- 开发一种基于深度学习的新方法,用于使用振动信号诊断干型变压器的故障.
- 通过先进的信号处理和机器学习,提高变压器故障诊断的准确性和效率.
- 解决复杂的操作环境在变压器故障检测中所带来的挑战.
主要方法:
- 创建了一个实验设置来模拟各种变压器故障并收集振动数据.
- 持续波纹转换 (CWT) 用于特征提取,将振动信号转换为时间频率 (RGB) 图像.
- 一个改进的卷积神经网络 (CNN) 模型被开发和优化,用于对变压器故障的图像识别.
主要成果:
- 拟议的CNN模型在故障诊断方面实现了99.95%的卓越整体准确性.
- 与其他机器学习技术相比,深度学习方法表现出卓越的性能.
- 使用CWT的特征提取有效地从振动信号中捕获故障特定信息.
结论:
- 开发的支持深度学习的方法为干式变压器故障诊断提供了高度准确和有效的解决方案.
- 振动分析与CWT和CNN相结合,为智能变压器监控提供了一个强大的方法.
- 这种智能诊断系统显著提高了变压器的安全性和运行可靠性.
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