دورية أكاديمية
A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis
| العنوان: | A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis |
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| المؤلفون: | Yichuan Shao, Can Zhang, Lei Xing, Haijing Sun, Qian Zhao, Le Zhang |
| المصدر: | Energy and AI, Vol 16, Iss , Pp 100349- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Computer software |
| مصطلحات موضوعية: | Solar photovoltaic panels, Dust detection, Pytorch, Adam improved algorithm, Economic benefits, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765 |
| الوصف: | Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency, leading to reduced energy generation. Regular monitoring and cleaning of solar photovoltaic panels is essential. Thus, developing optimal procedures for their upkeep is crucial for improving component efficiency, reducing maintenance costs, and conserving resources. This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels. Although the traditional Adam algorithm is the preferred choice for optimizing neural network models, it occasionally encounters problems such as local optima, overfitting, and not convergence due to inconsistent learning rates during the optimization process. To mitigate these issues, the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm, that allows for a gradual increase in the learning rate, ensuring stability in the preliminary phases of training. Concurrently, the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate. This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model. When applied on the dust detection on the surface of solar photovoltaic panels, this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method. Remarkably, it displayed noteworthy improvements within three distinct neural network frameworks: ResNet-18, VGG-16, and MobileNetV2, thereby attesting to the effectiveness of the novel algorithm. These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels. These research results will create economic benefits for enterprises and individuals, and are an important strategic development direction for the country. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-5468 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2666546824000156Test; https://doaj.org/toc/2666-5468Test |
| DOI: | 10.1016/j.egyai.2024.100349 |
| الوصول الحر: | https://doaj.org/article/a2529563ce9b4802be2fc754d9cc3b29Test |
| رقم الانضمام: | edsdoj.2529563ce9b4802be2fc754d9cc3b29 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26665468 |
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| DOI: | 10.1016/j.egyai.2024.100349 |