通过人工智能集成与实验性直流到直流Buck Boost转换器实现增强的光伏面板诊断
Chouaib Labiod1,2, Redha Meneceur3, Ali Bebboukha3
1Department of Mechanical Engineering, University of El Oued, El Oued, 39000, Algeria.
Scientific reports
|January 3, 2025
概括
本研究介绍了一种使用哈里斯·霍克斯优化 (HHO) 算法和XGBoost机器学习进行光伏 (PV) 系统故障检测的新方法. 该HHO-XGBoost模型在识别光伏电池板故障方面达到99%以上的准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 电力电子 电力电子 电力电子
背景情况:
- 光伏 (PV) 系统健康监测对于能源效率和可靠性至关重要.
- 太阳能系统的故障会导致电流-电压 (I-V) 特性显著偏差.
- 精确的故障检测确保了光伏发电厂的最佳性能和维护.
研究的目的:
- 开发使用I-V曲线的光伏电池板的先进诊断方法.
- 通过结合优化和基于分类的人工智能技术来增强故障检测.
- 为了提高光伏系统故障诊断的准确性和可靠性.
主要方法:
- 实现一个直流/直流反弹转换器,用于实时采集光伏系统数据 (I-V测量).
- 应用哈里斯·霍克斯优化 (HHO) 算法来识别实验数据集中的关键特征.
- 将HHO算法与XGBoost机器学习模型集成,用于故障检测.
主要成果:
- 拟议的HHO-XGBoost算法在检测光伏系统故障方面实现了99.49%的准确性.
- 该模型与其他人工智能方法 (如GADF-ANN,PCA-SVM,PNN和Fuzzy Logic) 相比,表现出更好的性能.
- 验证证实,在诊断光伏系统状况方面,整体准确率为98.48%.
结论:
- HHO-XGBoost算法是非常有效和强大的,用于在光伏系统中准确检测和分类故障.
- 这种方法显著提高了光伏发电厂的可靠性和诊断能力.
- 该研究验证了优化光伏系统运行寿命和性能的先进方法.
关键词:
人工智能的人工智能电流/电流转换器 DC/DC 转换器故障检测 检测故障检测功能选择 功能选择哈里斯·霍克斯的优化优化的I-V特征.机器学习 机器学习光伏系统 光伏系统实时数据监控 实时数据监控在XGBoost中使用.更多相关视频
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