机器学习的实验验证,用于污染的高压绝缘体的污染分类,使用泄漏电流
Umer Amir Khan1,2, Mansoor Asif3, Muhammad Hamza Zafar4
1Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia. umeramir.khan@kfupm.edu.sa.
Scientific reports
|April 17, 2025
概括
机器学习准确地使用泄漏电流数据对污染的高压绝缘体进行分类. 决策树模型提供更快的培训和优化,增强电力基础设施的预测性维护策略.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 高压绝缘器对于电力系统的可靠性至关重要.
- 在绝缘体上积聚的污染物会导致闪电和停电.
- 准确及时检测绝缘体污染对于防止故障至关重要.
研究的目的:
- 实验验证机器学习的使用,用于对高压绝缘体的污染水平进行分类.
- 使用泄漏电流数据评估不同机器学习模型的性能.
- 调查环境因素对污染分类准确性的影响.
主要方法:
- 在不同的污染,温度和湿度下,我们创建了一个来自器绝缘体的泄漏电流数据集.
- 特性提取在时间,频率和时间频率领域进行.
- 四个机器学习模型 (包括决策树和神经网络) 使用贝叶斯优化进行训练和优化.
- 模型性能是根据分类准确性来评估的.
主要成果:
- 机器学习模型的分类准确度始终超过98%.
- 基于决策树的模型显示,与神经网络相比,训练和优化时间要快得多.
- 加入温度和湿度数据提高了数据集的现实性和适用性.
结论:
- 机器学习对于使用泄漏电流对绝缘体污染进行分类非常有效.
- 决策树模型为此应用提供了神经网络的有效替代方案.
- 这些发现支持开发高压绝缘体的先进预测性维护策略.
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