Research Article Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing S. Naveen Venkatesh , 1 P. Arun Balaji , 1 Ganjikunta Chakrapani, 1 K. Annamalai, 1 S. Aravinth, 1 P. S. Anoop, 1 V. Sugumaran , 1 and Vetriselvi Mahamuni 2 1 School of Mechanical Engineering (SMEC), VIT University-Chennai Campus, Chennai, India 2 Department of Project Management, Mettu University, P.O. Box: 318, Mettu, Ethiopia Correspondence should be addressed to Vetriselvi Mahamuni; vetriselvi.m@meu.edu.et Received 5 October 2022; Revised 8 December 2022; Accepted 20 April 2023; Published 9 June 2023 Academic Editor: Punit Gupta Copyright © 2023 S. Naveen Venkatesh et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. Tis degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. Tis paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). Te algorithm used demonstrates a binary classifcation with high accuracy by comparing individual faults with good condition. Efcient and efective fault detection can be observed from the results obtained. 1. Introduction Demand for clean energy was created across the globe due to the advancements in technology, energy demand for over- growing population, and elevated pollution levels due to fossil fuel usage. Consequently, among various renewable energy sources available, solar energy is considered as the prime option to handle the challenges the world encounters. In recent times, power generation using photovoltaics (PVs) has grabbed huge attention due to the versatile application and socioeconomic benefts. Te process of converting solar energy into electricity is carried out with the aid of pho- tovoltaic modules (PVMs). Te International Energy Agency in their annual reports states that the annual global PV installations have seen a marginal growth of 45% from 36% with a total capacity of 770 GW by the end of 2022. Te growing PV market demands uninterrupted power supply, thereby necessitating efcient PVM operation. In general, PVMs are operated outdoors and in harsh climatic condi- tions that infuence the occurrence of faults in PVM. PVM faults can deteriorate the operational life span and reliability of the modules. Furthermore, to preserve the operational life span and reliability of PVM, timely and adequate moni- toring of PVM is necessary [1]. PVM faults occur as a consequence of thermal stresses, physical damage, mois- ture interference, short circuits, soiling, corrosion, and partial shading. Te presence of such fault results in the rise of a scenario termed as potential induced degradation (PID) that hinders the performance and lifespan of PVM [2]. Furthermore, to preserve the performance of PVM and ensure prolonged operation, early detection and continuous monitoring are essential. Conventionally, fault diagnosis was performed through visual inspections by skilled pro- fessionals. Recently, numerous PVM fault diagnosis tech- niques were adopted, namely, outdoor thermography, photoluminescence, electrical measurements, fuorescence imaging, and electroluminescence imaging [3]. However, such inspections demand higher time consumption, fatigue prone, more capital cost, large manpower, and non- applicability over large farms. Te setbacks mentioned Hindawi Scientific Programming Volume 2023, Article ID 8805817, 10 pages https://doi.org/10.1155/2023/8805817