基于机器学习的多方法解释,以增强用于功率变压器故障诊断的溶解气体分析
Suwarno1, Heri Sutikno1,2, Rahman Azis Prasojo3
1School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia.
Heliyon
|February 21, 2024
概括
本研究引入了一种用于功率变压器的新溶气分析 (DGA) 解释方法. 结合多种技术和机器学习,提高了评估变压器健康状况的准确性和一致性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 精确的溶气分析 (DGA) 对功率变压器的可靠性至关重要.
- 现有的DGA解释方法 (DRM,RRM,IRM,DTM,DPM) 可以产生相互矛盾的结果并遇到局限性.
- 不一致的DGA解释对变压器管理和运行安全构成风险.
研究的目的:
- 开发一种新的,更准确,更一致的DGA解释技术.
- 将已建立的DGA方法集成到一个统一的框架中.
- 为了提高功率变压器状态评估的可靠性.
主要方法:
- 一种多方法方法,整合现有的DGA解释技术.
- 实施评分指数和随机森林机器学习原则.
- 在不同操作条件下使用变压器的DGA数据进行验证.
主要成果:
- 与单个常规方法相比,提出的多方法DGA技术显示出更高的准确性和一致性.
- 基于随机森林的多方法实现了比单独使用评分指数更高的准确性.
- 新方法有效地解决了基于比率的DGA解释的局限性.
结论:
- 综合多方法DGA方法在变压器健康评估中提供了显著的改进.
- 机器学习,特别是随机森林,增强了DGA解释的预测能力.
- 这种新的技术通过更好的变压器管理,有助于更可靠的电力系统运行.
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