Authors : Harisree Haridas; Hruday Reddy K; Jahnavi P; Lochana B; Akshaya Acharya; Deba Chandan Mohanty; Bharani Kumar Depuru

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/mvyytmmu

Scribd : https://tinyurl.com/5n7xhnj2

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP309

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the solar energy sector, the task of monitoring and maintaining large photovoltaic (PV) system portfolios is essential for ensuring optimal performance and reliability. Prominent solar energy companies face challenges with their current fault detection methods, which are inefficient and resource- intensive. This paper addresses the critical need for improved fault detection in solar PV systems to maximize uptime and minimize maintenance costs. We employed advanced data preprocessing and augmentation techniques using Roboflow and developed a YOLOv8 segmentation model in Google Colab with GPU. This model was then deployed using Streamlit, providing a robust solution for identifying faulty solar modules. The proposed approach significantly enhances fault detection accuracy, achieving a minimum accuracy rate of 85%, thus ensuring reliable operation of the PV systems. Additionally, the implementation of this model contributes to a 15% reduction in system downtime and a 10% reduction in maintenance costs. By leveraging advanced machine learning techniques, our solution transforms the maintenance process, making it more efficient and cost-effective. Consequently, this work not only improves the reliability and performance of solar PV systems but also supports the broader goal of sustainable energy through more efficient resource usage.

Keywords : Solar PV Systems, Fault Detection, Machine Learning, Data Preprocessing, Roboflow, YOLOV8, Google- Colab, GPU, Streamlit.

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