基于人工神经网络的太阳能板污损控制

Sujit Kumar, V. Dave
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引用次数: 4

摘要

光伏电池技术的迅速发展使太阳能的安装和利用变得非常容易。由半导体制造的太阳能电池已经将光伏系统的效率降低了15-20%。此外,太阳能电池板的性能仍然会因灰尘的积累而降低。介绍了一种利用基于Levenberg - Marquardt (LM)神经网络的自动雨刷控制模型清洗太阳能电池板的技术。利用专家信息数据对基于LM算法的模式识别网络模型进行了训练和测试。结果表明,该基于LM神经网络的模型在不使用数学模型的情况下,能有效地控制自动雨刷系统的疑点和非线性。所建立的人工神经网络模型预测面板上积尘的准确率为99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANN based controller to mitigate soiling loss on solar panels
The hasty augmentation in PV cells technology made the installation and utilization of solar power very trouble-free. The solar cells fabricated by semiconductors, has reduced the efficiency of PV systems to 15–20%. Further, the performance of solar panel is still reduced by the accumulation of dust known as soiling. This paper introduces a technique of cleaning a solar panel using control model of automatic windshield wiper based on Levenberg — Marquardt (LM) neural network. A network model of pattern recognition based on LM algorithm is trained and tested with specialist's information data. The result showed that this model based on LM neural network is effectual to grip doubts and nonlinearities of the automatic windshield wiper system, without using mathematical model. The accuracy of developed ANN model to predict the accumulated dust over the panel was found to be 99%.
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