Department of Electrical Engineering, University of Wah, Wah Cantt 47040, Pakistan.
Department of Robotics and Intelligent Machine Engineering (RIME), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST) H-12, Islamabad 44000, Pakistan.
Sensors (Basel). 2021 Aug 23;21(16):5668. doi: 10.3390/s21165668.
Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system's memory, resulting in savings in the PV investment.
有缺陷的光伏电池板会降低整个光伏串的效率，通过降低效率和使用寿命来减少投资。在这项研究中，首先，我们从头开始准备了一个孤立卷积神经网络模型（ICNM），根据光伏电池板的健康状况，即健康、热点和故障，对其红外图像进行分类。与经过迁移学习预训练的 ShuffleNet、GoogleNet 和 SqueezeNet 模型相比，ICNM 占用的内存最少，其架构也最简单，执行时间最短，准确率为 96%。之后，我们基于 ICNM 的优势，通过迁移学习将光伏电池板的缺陷重新分类为五类，即鸟粪、单块、补丁、水平对齐串和块状，测试准确率为 97.62%。该方法可以根据光伏电池板的健康状况和缺陷更快地进行识别和分类，具有较高的准确率，并且占用的系统内存最少，从而节省光伏投资。
