论文标题
使用卷积神经网络的PV太阳能电池板的故障检测方案
A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy
论文作者
论文摘要
太阳能是最可靠的可再生能源技术之一，因为它几乎在全球范围内都是可行的。但是，提高太阳能光伏系统的效率仍然是一个重大挑战。为了增强太阳系的鲁棒性，本文提出了基于训练的卷积神经网络（CNN）的故障检测方案，以划分光伏模块的图像。对于二进制分类，该算法将PV细胞的输入图像分为两类（即错误或正常）。为了进一步评估网络的能力，将有缺陷的PV细胞组织为阴影，破裂或尘土飞扬的细胞，并将模型用于多种分类。二进制分类的建议CNN模型的成功率为91.1％，多分类为88.6％。因此，所提出的训练有素的CNN模型明显胜过先前使用相同数据集的研究中介绍的CNN模型。提出的基于CNN的故障检测模型是直接，简单且有效的，可以应用于太阳能电池板的故障检测。
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.
