基于主动学习的变压器故障诊断研究,使用不平衡的石油中溶解的气体数据进行研究
Pengfei Tang1, Zhonghao Zhang1, Jie Tong1
1China Electric Power Research Institute, Haidian District, Beijing 100192, China.
The Review of scientific instruments
|May 1, 2024
概括
本研究引入了一个主动学习框架,以增强使用溶解气体分析的功率变压器故障诊断. 新方法提高了预测准确性,减少了数据标签需求,即使数据集有限或不平衡.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 电力变压器是关键基础设施;故障对电力系统构成重大风险.
- 溶解气体分析 (DGA) 对于变压器健康监测至关重要.
- 目前用于DGA的AI方法与有限或不平衡的数据集作斗争.
研究的目的:
- 为电力变压器DGA开发一个积极的学习框架.
- 解决DGA中的数据驱动AI算法的局限性.
- 为了提高变压器故障诊断的准确性和效率.
主要方法:
- 提出了动态模型培训的积极学习框架.
- 加强了积极学习的空间搜索策略.
- 使用样本特征差异的乘积来测量样本差异.
主要成果:
- 积极学习框架根据数据特征动态训练模型.
- 改进的空间搜索策略提高了模型性能.
- 与被动学习相比,实现了数据标签工作的显著减少.
结论:
- 提出的积极学习框架有效地提高了功率变压器DGA的预测准确性.
- 这种方法为变压器故障诊断提供了更有效的解决方案,特别是在数据稀缺的情况下.
- 增强的战略提供了一个强大的方法来处理在关键基础设施监测中不平衡的数据集.
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