LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification

Abstract

Solar-cell panels use sunlight as a source of energy to generate electricity. However, the performances of solar panels decline when they degrade, owing to defects. Some common defects in solar-cell panels include hot spots, cracking, and dust. Hence, it is important to efficiently detect defects in solar-cell panels and repair them. In this study, we propose a lightweight inception residual convolutional network (LIRNet) to detect defects in solar-cell panels. LIRNet is a neural network model that utilizes deep learning techniques. To achieve high model performance on solar panels, including high fault detection accuracy and processing speed, LIRNet draws on hierarchical learning, which is a two-phase solar-panel-defect classification method. The first phase is the data-preprocessing stage. We use the K-means clustering algorithm to refine the dataset. The second phase is the training of the model. We designed a powerful and lightweight neural network model to enhance accuracy and speed up the training time. In the experiment, LIRNet improved the accuracy by approximately 8% and performed ten times faster than EfficientNet.

Fault Detection in Solar Energy Systems: A Deep Learning Approach

“…Chen et al [59] developed a ShuffleNet V2 network for the infrared images of 11 types of PV module faults, and an accuracy of 84.06% was achieved. Lee et al [60] attempted to detect defects in PV panels; they achieved 89% accuracy with the residual convolutional network they proposed. Açıkgöz et al [61] studied only hot spot classification among solar panel failures and achieved an accuracy value of 98.65% with AlexNet.…”