整合机器学习和有限元方法来评估光伏模块中的刚性降解
1School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, People's Republic of China.
概括
一个新的机器学习模型准确地预测了裂光伏模块的刚性降解. 这种先进的堆叠自动编码方法超越了传统技术,提供精确的降解洞察力和参数控制,以提高模块的寿命.
科学领域:
- 材料科学与工程 材料科学与工程
- 可再生能源技术可再生能源技术
- 机器学习应用 机器学习应用
背景情况:
- 太阳能 (PV) 模块容易受到刚性降解的影响,特别是当已经存在裂时.
- 准确预测这种退化对于评估模块寿命和性能可靠性至关重要.
- 现有的预测方法往往缺乏复杂降解动态所需的精度.
研究的目的:
- 引入和验证一种新的机器学习 (ML) 方法,用于预测破裂光伏模块的刚性降解.
- 将拟议的ML模型与传统方法的性能进行比较.
- 确定影响降解的关键输入参数,并为控制的降解水平划定参数空间.
主要方法:
- 开发一个堆叠的自编码神经网络,用于硬度降解的预测.
- 使用PV模块行为的全面数值模拟数据来训练ML模型.
- 对人工神经网络,空间矢量机器和随机森林模型进行比较分析.
主要成果:
- 堆叠的自动编码器模型实现了0.961的高R2和4.02%的低RMSE,明显优于其他方法.
- 降解预测的准确性受到气候温度,颗粒大小,材料力度和裂纹前大小等输入参数的影响.
- 几何分析显示,模型高估了更厚的玻璃,低估了更薄的晶片和乙烯基酸盐层.
结论:
- 拟议的堆叠自动编码器ML模型提供了一个非常准确和强大的方法来预测裂纹光伏模块的刚性降解.
- 该模型不仅预测了降解,而且还提供了对所需降解水平所需的参数空间 (例如温度,裂大小) 的洞察.
- 这种预测能力可以为维护策略提供信息,并提高光伏能源系统的长期可靠性.
相关概念视频
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01:23Stresses under Combined Loadings
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