Abstract

A key component of the transition towards cleaner and more sustainable power sources, driven by the global demand for such energy, has been the fast improvement in the installation of solar photovoltaic (PV) plants. Ensuring the dependability of photovoltaic panels becomes crucially important when solar installations develop in scale. Photovoltaic panels have flaws that reduce their effectiveness and shorten their lifespan, but they are durable and long-lasting otherwise. Solar plants need to work as efficiently as possible with low downtime and to want solar energy to be viable in the long run, the issues shall be fined a fixed promptly. Quality monitoring has always relied on human inspectors. The speed, accuracy, and scalability issues that plague human inspection methods become more apparent as solar projects expand in size. The goal of this research is to improve solar panel flaw identification using cutting-edge image processing techniques. Getting beyond these problems is its aim. The project used Convolutional neural network (CNN). We will use ResNet50. ResNet50 is a convolutional neural network (CNN). It is a specific architecture within the CNN family that is designed for tasks related to computer vision, such as image classification, object detection, and image segmentation. The datasets that will be used in these sections are collected from Kaggle and from DEWA Solar PV Plants. The data will be subjected to Data Preprocessing, Data Cleaning and Organization, Data Augmentation and Feature Extraction. further building the model and go through the training, validation and testing process to ensure a high-performance model that accurately predicts the defects.

Library of Congress Subject Headings

Solar panels--Quality control; Image processing--Digital techniques; Neural networks (Computer science); Convolutions (Mathematics)

Publication Date

5-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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