对风力轮机变压器及其在溶解气体评估中的极限进行全面分析
Ricardo Manuel Arias Velásquez1
1Universidad Tecnológica Del Perú, Peru.
Heliyon
|November 5, 2024
概括
这项研究审查了使用溶解气体分析 (DGA) 的变压器故障诊断. 机器学习模型在预测变压器故障方面实现了超过95%的准确性,提高了可靠性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 变压器故障对电网构成重大风险.
- 溶解气体分析 (DGA) 是监测变压器健康状况的关键方法.
- 现有的诊断方法需要加强,以提高准确性和可靠性.
研究的目的:
- 使用DGA数据系统地审查变压器故障诊断.
- 分析来自风电场变压器的DGA数据.
- 评估用于故障预测的机器学习算法.
主要方法:
- 按照PRISMA-A方法的系统文献审查.
- 从四个主要数据库中提取DGA样本的数据提取和统计分析.
- 机器学习算法的应用和评估 (随机森林,CART) 用于故障预测.
主要成果:
- 分析了来自风电场变压器的1190件物品和4810个DGA样本.
- 确定关键气体度 (H2,CH4,CO,CO2,C2H2,C2H4,C2H6) 作为故障指标.
- 机器学习模型在变压器故障预测中达到高达95.29%的准确性.
结论:
- 先进的分析和机器学习显著提高了变压器故障诊断.
- 对IEEE气体度值的拟议修订可以改善早期故障检测.
- 研究结果支持预测性维护策略,以提高变压器可靠性和电网稳定性.
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