数字双胞胎方法用于光伏电站故障诊断DC-DC转换器DC-DC转换器
Pablo José Hueros-Barrios1, Francisco Javier Rodríguez Sánchez1, Pedro Martín Sánchez1
1Department of Electronics, University of Alcalá, 28801 Madrid, Spain.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了光伏DC-DC转换器的混合故障诊断框架,集成数字双胞胎技术和机器学习. 它有效地检测硬件故障和网络攻击,确保可靠的太阳能系统.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 网络物理系统 网络物理系统
背景情况:
- 在光伏 (PV) 系统中,直流-直流转换器至关重要,但易受硬件故障和虚假数据注入 (FDIA) 等网络攻击的影响.
- 现有的故障诊断方法可能无法充分解决实时光伏系统中混合威胁的复杂性.
研究的目的:
- 开发和验证光伏系统中DC-DC转换器的混合故障诊断框架.
- 将数字双胞胎 (DT) 建模与机器学习相结合,以进行可靠的故障检测和分类.
- 解决硬件故障 (例如,IGBT,二极管故障) 和传感器级的FDIA.
主要方法:
- 一个五维的数字双胞胎架构,使用符合FMI的FMU与实时模拟的物理工厂进行交互.
- 通过比较实时系统行为与使用动态值的DT预测来检测故障.
- 在具有统计和光谱描述符的规范时间序列窗口上使用二级分类器进行故障分类.
- 合成数据生成使用模拟用于培训.
主要成果:
- 在故障诊断中实现了高分类准确性.
- 证明了对环境波动的坚固性.
- 在不同的系统配置中验证了可转移性.
- 确认与低成本部署硬件的兼容性.
结论:
- 拟议的混合框架为光伏系统故障诊断提供了实用和有效的解决方案.
- 数字双胞胎和机器学习的整合提高了直流-直流转换器的可靠性和安全性.
- 该框架在Hardware in the Loop (HIL) 模拟中的性能表明其在现实世界中具有强大的适用性.
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