在变电站4.0中智能监控电力变压器:多传感器集成和机器学习方法
Fabio Henrique de Souza Duz1, Tiago Goncalves Zacarias1, Ronny Francis Ribeiro Junior1
1R&D Department, Gnarus Institute, Itajuba 37500-052, MG, Brazil.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了电力变压器的先进监控系统,它结合了多个传感器和机器学习,用于早期故障检测. 这种综合方法提高了电网可靠性,并减少了因停电造成的经济损失.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 机器学习应用 机器学习应用
背景情况:
- 电力变压器故障会导致严重的停电和经济损失.
- 传统的离线维护对于现代数字变电站来说是不够的.
- 需要先进的实时监控解决方案.
研究的目的:
- 为功率变压器开发一个集成的多传感器监控框架.
- 通过机器学习实现自动故障诊断.
- 提高电力系统的可靠性和安全性.
主要方法:
- 综合多传感器监测:在线频率响应分析 (OnFRA® 4.0),电容式自来水管式监测 (FRACTIVE® 4.0),溶气分析,温度测量.
- 在SCADA系统中的数据同步和管理.
- 随机森林分类器通过模拟降解数据进行训练,并使用主要组件分析来减少维度.
主要成果:
- 综合框架成功地将高保真监控硬件与机器学习相结合.
- 随机森林模型在模拟数据上实现了完美的分类指标,用于异常检测.
- 监控基础设施通过实验室实验来验证.
结论:
- 拟议的框架证明了在功率变压器中自动故障诊断的可行性.
- 持续的数据采集和分析增强了预测性维护能力.
- 这种方法对于确保现代数字变电站的可靠性至关重要.
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