基于卷积神经网络的高精度光伏太阳能板故障检测方案
A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy
AI总结：
本文提出基于CNN的光伏板故障检测方案，通过二分类和多分类实现高精度故障识别，效果优于先前研究。
摘要：
太阳能是全球最可靠的可再生能源技术之一，几乎在世界各地都能实现。然而，提高太阳能光伏系统效率仍然是重大挑战。为了增强太阳能系统的鲁棒性，本文提出了一种经过训练的卷积神经网络（CNN）基于故障检测方案，用于分割光伏模块的图像。对于二分类，算法将输入的光伏电池图像分为两个类别（即故障或正常）。为进一步评估网络的能力，缺陷的光伏电池被组织成阴影、开裂或尘土细胞，并利用模型进行多分类。所提出的CNN模型在二分类中的成功率为91.1%，在多分类中的成功率为88.6%。因此，所提出的训练CNN模型在二分类和多分类任务中均显著优于之前研究中使用相同数据集的CNN模型。所提出的基于CNN的故障检测模型简单且有效，可用于太阳能板的故障检测。
英文摘要：
Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the solar system, this paper proposes a trained convolutional neural network (CNN) based fault detection scheme to divide the images of photovoltaic modules. For binary classification, the algorithm classifies the input images of PV cells into two categories (i.e. faulty or normal). To further assess the network's capability, the defective PV cells are organized into shadowy, cracked, or dusty cells, and the model is utilized for multiple classifications. The success rate for the proposed CNN model is 91.1% for binary classification and 88.6% for multi-classification. Thus, the proposed trained CNN model remarkably outperforms the CNN model presented in a previous study which used the same datasets. The proposed CNN-based fault detection model is straightforward, simple and effective and could be applied in the fault detection of solar panel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
