油浸式变压器的故障诊断方法 集成数字双胞胎模型
Haiyan Yao1, Xin Zhang2, Qiang Guo1
1Hangzhou Electric Power Equipment Manufacturing Co. Ltd Yuhang Qunli Complete Sets Electricity Manufacturing Branch Electric, Hangzhou, 311000, China.
Scientific reports
|September 2, 2024
概括
这项研究引入了一种新的数字双一体化故障诊断方法,用于浸油变压器,达到98.1013%的准确性. 该方法通过改进状态感知和实时协作来增强变压器维护.
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:
- 变压器故障诊断的低准确性阻碍了可靠的操作.
- 糟糕的状态感知和有限的实时协作阻碍了有效的维护.
- 现有的方法难以在浸油变压器中进行复杂的故障识别.
研究的目的:
- 为浸油变压器开发一个准确和稳定的故障诊断方法.
- 整合数字双胞胎技术进行实时监测和诊断.
- 为了提高变压器的智能操作和维护.
主要方法:
- 使用代近邻超采样 (INNOS) 进行故障样本平衡.
- 提取九维比特征和与溶解气体的相关性分析.
- 通过稀疏主要组件分析 (SPCA) 进行特征融合和维度减小.
- 使用Aquila Optimizer (AO) 对AO-KELM模型进行内核极端学习机器 (KELM) 参数的优化.
主要成果:
- 拟议的AO-KELM模型实现了高故障诊断准确率的98.1013%.
- 与数字双胞胎模型的集成使物理和虚拟变压器空间之间的实时交互成为可能.
- 该方法表现出高诊断准确度和实验结果的稳定性.
结论:
- 数字双胞胎集成故障诊断方法显著提高了准确性和稳定性.
- 启用实时在线诊断,增强变压器的操作智能.
- 这种方法为变压器的智能操作和维护提供了有价值的参考.
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