基于机器学习的级联h桥逆变器的最佳主动无监督故障检测
Ashkan Safari1, Mohammad Hosein Tehranidoost2, Mehran Sabahi2
1Faculty of Electrical and Computer Engineering , University of Tabriz , Tabriz, Iran. ashkansafari@ieee.org.
Scientific reports
|May 3, 2025
概括
本研究介绍了一种孤立森林 (IF) 机器学习模型,用于可靠地检测多级逆变器 (MLI) 的故障. 投资理财模型提供了一种高效,无监督的方法来识别系统故障,提高工业应用的可靠性.
科学领域:
- 电气工程 电气工程
- 电力电子 电力电子 电力电子
- 机器学习 机器学习
背景情况:
- 多级逆变器 (MLI) 对高压,高功率应用至关重要,但由于更多的开关导致故障发生的增加,它们的可靠性受到挑战.
- 有效的故障检测对于工业环境中的MLI可靠性至关重要,但由于参数不确定性,传统的基于物理和基于模型的方法面临挑战.
- 由于MLI系统的复杂性,需要先进的故障检测技术来确保运行完整性.
研究的目的:
- 提出和评估一个高效的,无监督的机器学习模型,用于MLIs中的故障检测.
- 为了证明隔离森林 (IF) 算法的有效性,用于识别17级级联H桥 (CHB) 逆变器中的故障.
- 用关键绩效指标比较IF模型与其他方法的表现.
主要方法:
- 实现隔离森林 (IF) 算法,这是用于异常检测的无监督机器学习技术.
- 在各种故障条件下模拟一个17级级联H桥 (CHB) 逆变器.
- 使用F1-Score,精度,回忆和准确度指标对IF模型性能进行比较分析.
主要成果:
- 隔离森林 (IF) 模型在模拟的17级CHB逆变器中检测故障方面表现出高效率和准确性.
- 与基于F1-Score,精度,回忆和准确性的其他方法相比,IF模型取得了更高的性能.
- 拟议的无监督方法要求MLI系统的计算复杂性最小.
结论:
- 隔离森林 (IF) 模型为MLIs提供了一个准确而高效的无监督故障检测解决方案.
- 这种方法为完全自动化和自我修复的工业动力系统铺平了道路.
- 在故障检测方面,IF模型的有效性提高了MLI系统的整体可靠性和性能.
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