Year: 2023
Type: article
Abstract: The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This 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. This 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). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained.
Source: Scientific Programming
DOI: 10.1155/2023/8805817
Authors: S. Naveen Venkatesh, P. Arun Balaji, Ganjikunta Chakrapani, K. Annamalai, S. Aravinth, P. S. Anoop, V. Sugumaran, Vetriselvi Mahamuni
Institutions: Vellore Institute of Technology University, Mattu University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,000
OpenAlex ID: W4380085508