Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Waqas Ahmed; Aamir Hanif; Karam Dad Kallu; Abbas Z Kouzani; Muhammad Umair Ali; Amad Zafar

doi:10.3390/s21165668

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Sensors (Basel). 2021 Aug 23;21(16):5668. doi: 10.3390/s21165668.

Authors

Waqas Ahmed¹, Aamir Hanif¹, Karam Dad Kallu², Abbas Z Kouzani³, Muhammad Umair Ali⁴, Amad Zafar⁵

Affiliations

¹ Department of Electrical Engineering, University of Wah, Wah Cantt 47040, Pakistan.
² 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.
³ School of Engineering, Deakin University, Geelong, VIC 3216, Australia.
⁴ Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea.
⁵ Department of Electrical Engineering, Islamabad Campus, University of Lahore, Islamabad 54590, Pakistan.

Abstract

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.

Keywords: PV panels; deep convolution neural network; hotspots; infrared images.

MeSH terms

Diagnostic Imaging*