审查太阳能发电厂预测性维护和网络安全方面的最新进展
Younes Ledmaoui1, Adila El Maghraoui2, Mohamed El Aroussi1
1Laboratory Engineering System, Hassania School of Public Works, Casablanca BP 8108, Morocco.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本综述探讨了太阳能电池板预测性维护和网络安全方面的进展,强调物联网,机器学习和深度学习,用于早期缺陷检测和减少逆变器故障.
科学领域:
- 可再生能源系统可再生能源系统
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 太阳能电池板系统需要强大的维护策略,以确保最佳性能和寿命.
- 越来越复杂和数据生成需要先进的监测和诊断技术.
- 网络安全是相互连接的可再生能源基础设施日益关注的问题.
研究的目的:
- 系统地审查最近 (2018-2023) 太阳能电池板系统预测性维护方面的进展.
- 分析新兴预测性维护技术的优势和挑战.
- 检查在太阳能发电厂维护框架内整合网络安全措施的情况.
主要方法:
- 从2018年到2023年对研究研究,评论和经验分析的系统文献综述.
- 分析包括物联网 (IoT),机器学习 (ML) 和深度学习 (DL) 在内的技术.
- 在太阳能电池板中检测缺陷,降解分析和异常识别的评估.
主要成果:
- 预测性维护技术有效地检测缺陷和退化,减少逆变器故障.
- 物联网 (IoT),机器学习 (ML) 和深度学习 (DL) 增强了实时监控和异常识别.
- 关键的挑战包括平衡模型的复杂性和准确性,管理系统的不可预测性和适应环境变化.
结论:
- 先进的预测性维护,利用物联网,ML和DL,显著提高太阳能电池板系统的效率和可靠性.
- 解决采用方面的挑战对于可再生能源领域的广泛实施至关重要.
- 综合网络安全对于安全有效的太阳能发电厂运营至关重要.
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