使用V-P曲线衍生和LSTM-基于分类的光伏模块退化检测
Chan-Ho Lee1, Sang-Kil Lim1, Sung-Jun Park2
1Department of Electronic Engineering, Chosun University, Gwangju 61452, Republic of Korea.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了一种使用电压功率曲线分析和人工智能模型检测太阳能模块降解的新方法. 它可以早期识别出故障的太阳能电池板及其降解水平,以改善监控.
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.
- 材料科学用于能源.
背景情况:
- 光伏 (PV) 系统对于可持续能源至关重要,但由于环境暴露,其性能下降.
- 目前用于太阳能模块老化的诊断方法缺乏实时监测,定量评估,以及大型发电厂的可扩展性.
- 降解导致电力损失和运行问题,需要先进的检测技术.
研究的目的:
- 开发一种新的实时方法来检测和量化太阳能模块的降解.
- 为了能够及早识别一串中退化的太阳能模块的数量和严重程度.
- 克服现有的光伏诊断工具的局限性.
主要方法:
- 利用电压功率曲线的第一阶导数来提取关键的降解特征.
- 开发基于长期短期记忆 (LSTM) 的AI模型,用于分类正常/异常状态并预测衰老.
- 设计一个针对光伏时间序列数据优化的浅层LSTM网络,以防止过和梯度消失.
主要成果:
- 提出的方法有效地提取了太阳能模块退化迹象的特征.
- 该LSTM模型准确地分类系统状态,并预测老化状态,展示学习和融合.
- 马特拉布模拟验证了模型在训练中的有效性和稳定性.
结论:
- 新的电压功率曲线导数方法与LSTM AI相结合,为太阳能模块降解检测提供了强大的解决方案.
- 这种方法促进了对光伏系统健康状况的早期和准确诊断,提高了运营效率.
- 该研究为光伏发电厂的先进实时监控系统提供了基础.
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