基于格拉米安角场和优化并行ShuffleNetV2的变压器故障诊断方法.
Qiang Guo1, Haiyan Yao1, Yuefei Xu2
1Hangzhou Power Equipment Manufacturing Co., Ltd. Yuhang Qunli Complete Electrical Equipment Manufacturing Branch, Hangzhou, 310000, China.
Scientific reports
|July 3, 2025
概括
本研究介绍了一种使用格拉米安角场 (GASF/GADF) 图像和优化的ShuffleNetV2模型的新变压器故障诊断方法. 该方法显著提高了诊断的准确性和稳定性,克服了手工方法的局限性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 传统上,变压器故障诊断依赖于手工经验和单角特征提取,导致诊断准确度低.
- 现有的方法难以捕获全面的故障信息,限制了诊断的有效性.
- 需要自动化,准确和强大的变压器故障诊断技术.
研究的目的:
- 开发一种先进的变压器故障诊断方法,提高诊断准确度,克服手工经验和单角特征提取的局限性.
- 利用格拉米安角场 (GASF和GADF) 来实现故障信号的全面特征表示.
- 使用优化的并行ShuffleNetV2模型,以实现高效的特征提取和融合.
主要方法:
- 故障信号被转换为格拉米安角度超再缩放 (GASF) 和格拉米安角度差距场 (GADF) 图像,以捕获完整的故障信息.
- 优化双分支并行ShuffleNetV2模型用于同时从GASF和GADF图像中提取特征.
- 卷积块注意模块 (CBAM) 集成用于自适应加权特征融合,随后进行SoftMax分类.
主要成果:
- 拟议的方法在变压器故障诊断中实现了99.13%的高整体准确性.
- 实验结果表明,开发的模型具有强大的稳定性和概括性.
- 与使用单角特征图像的方法相比,这种方法显著提高了诊断效率.
结论:
- 拟议的变压器故障诊断方法有效地解决了手工经验和单角特征提取的局限性.
- 整合GASF/GADF成像和优化的ShuffleNetV2在诊断准确性和可靠性方面取得了重大进展.
- 这种方法为自动化变压器故障诊断提供了强大而有效的解决方案.
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