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结合生产数据时间序列与红外热成像评估热特征对光伏输出随时间影响
Combining Production Data Timeseries and Infrared Thermography to Assess the Impact of Thermal Signatures on Photovoltaic Yield Over Time
| 作者 | Bjørn Lupton Aarseth · Magnus Moe Nygård · Gaute Otnes · Erik Stensrud Marstein |
| 期刊 | IEEE Journal of Photovoltaics |
| 出版日期 | 2024年11月 |
| 技术分类 | 光伏发电技术 |
| 技术标签 | 储能系统 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | 光伏模块 红外热成像 功率损失 长期监测 降解模式 |
语言:
中文摘要
具有热特征的光伏(PV)组件可通过红外热成像技术(IRT)进行检测,并且可以通过分析相应的发电量时间序列数据来估算这些组件造成的功率损失。在本研究中,我们结合这些方法分析了光伏组件降解模式对一座75兆瓦峰值(MWp)光伏电站整体发电的影响。我们发现,运行5年后,有0.2%的光伏组件出现热特征,且这些热特征导致电站发电量降低了0.06%。对于所研究的电站,我们计算得出,进行红外热成像扫描并随后更换受热特征影响的组件,其投资回收期超过10年。然而,与热特征相关的功率损失似乎会随时间呈非线性发展。这凸显了持续长期监测的重要性:它能够监测性能与质保期限的关系,并有助于确定为实现经济高效的运维策略而需要进行的更换行动的优先级。要了解光伏组件的降解模式、其随时间的变化情况以及其对组件技术和气候的依赖性,也需要这些信息。
English Abstract
Photovoltaic (PV) modules with thermal signatures can be detected by infrared thermography (IRT) and the resulting power loss from these modules can be estimated through analysis of corresponding energy yield time series data. In the present work, we combine these methods to analyze the effect of PV module degradation modes on the overall energy generation in a 75 MWp PV power plant. We find that 0.2% of the PV modules are affected by thermal signatures after 5 years of operation and that the thermal signatures lead to a 0.06% reduction in power plant yield. We calculate a payback time of the IRT scan and subsequent replacement of modules affected by thermal signatures of more than 10 years for the investigated power plant. However, the power loss associated with thermal signatures seems to develop nonlinearly over time. This underlines the importance of continuous, long-term monitoring: it enables monitoring of performance in relation to warranty limits and supports prioritization of replacement actions required for cost-effective operations and maintenance strategies. This information is also required to understand PV module degradation modes, their time dependence and their dependence on module technology and climates.
S
SunView 深度解读
该研究将红外热成像与发电数据时间序列结合的故障诊断方法,对阳光电源iSolarCloud智能运维平台具有重要应用价值。可直接集成到SG系列光伏逆变器的智能诊断系统中,通过MPPT算法监测的组串电流-电压曲线异常与热特征关联分析,实现热斑故障的早期预警。该时变影响量化模型可优化预测性维护策略,提升PowerTitan大型储能系统中光伏侧的发电效率评估精度。研究建立的热特征演变与发电量衰减关联模型,可为阳光电源ESS集成方案提供更精准的系统寿命预测与运维决策支持,降低LCOE并提升电站资产管理水平。