通过拉曼光谱和优化支矢量机器对变压器油屏障绝缘的老化状态的评估
Deliang Liu1, Biao Lu1, Wenping Wu1
1School of Information and Engineering, Suzhou University, Suzhou 234000, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
评估变压器油屏隔热的老化对于电力系统的可靠性至关重要. 这项研究使用拉曼光谱和机器学习来准确地分类绝缘体老化状态,达到94.44%的准确性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 频谱学是一种光谱学.
背景情况:
- 变压器绝缘降解会影响电力系统的安全性和可靠性.
- 精确评估浸油屏障绝缘材料的老化是非常重要的.
研究的目的:
- 开发和验证一种用于评估变压器油屏障绝缘体老化状态的方法.
- 使用拉曼光谱和机器学习来准确分类绝缘降解.
主要方法:
- 室内加速热衰老实验,以创建不同绝缘降解的样本.
- 拉曼光谱用于表征老化的浸油屏障绝缘.
- 数据处理包括基线校正,平滑和主要组件分析 (PCA).
- 使用网格搜索,PSO和GA优化参数 (C和gamma) 的支持向量机 (SVM) 分类.
主要成果:
- 拉曼光谱成功地描述了变压器绝缘的不同老化状态.
- 一个最佳的PCA子集功能增强了SVM分类准确性.
- 在外部验证样本中,SVM分类器实现了高分类准确率94.44%.
- 网格搜索被证明是优化SVM参数的有效方法.
结论:
- 开发的拉曼光谱和基于SVM的方法提供了有效的评估石油屏障绝缘老化.
- 这种方法为电力系统的状态监测和维护策略提供了技术支持.
- 这些发现有助于确保电力基础设施的可靠运行.
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