Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV systems for timely troubleshooting potential failure and effective cleaning scheme are the focus of current research works. In this study, I–V characteristics of PV strings under various fault states are analyzed, especially soiling condition. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine is proposed to handle this problem. The proposed algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Moreover, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 and 3.9 kWp are used to verify the proposed diagnosis method. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technology.

中文翻译：

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考虑沙尘影响的光伏故障诊断混合人工蜂群算法与半监督极限学习机设计

在室外环境中运行的光伏 (PV) 系统容易受到各种因素的影响，尤其是灰尘的影响。异常操作会导致大量电力损失，严重的短路故障可能会导致安全问题和火灾隐患。因此，监测光伏系统的运行状态以及时排除潜在故障和有效的清洁方案是当前研究工作的重点。在这项研究中，分析了各种故障状态下光伏组串的 I-V 特性，特别是污染条件。由于具有特定故障的光伏系统的标记数据难以记录，特别是在大规模系统中，因此提出了一种结合人工蜂群算法和半监督极限学习机的新算法来解决这个问题。该算法可以利用少量的模拟标记数据和历史未标记数据来诊断光伏故障，大大降低了人工成本和耗时。此外，灰尘堆积的监测可以提醒电厂业主及时清洁光伏组件，增加发电效益。3.51 和 3.9 kWp 的光伏系统用于验证所提出的诊断方法。数值模拟和实验结果都表明了所提出的光伏诊断技术的准确性和可靠性。9 kWp 用于验证所提出的诊断方法。数值模拟和实验结果都表明了所提出的光伏诊断技术的准确性和可靠性。9 kWp 用于验证所提出的诊断方法。数值模拟和实验结果都表明了所提出的光伏诊断技术的准确性和可靠性。