Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays.

Arifeen, Murshedul; Petrovski, Andrei; Hasan, Md Junayed; Noman, Khandaker; Navid, Wasib Ul; Haruna, Auwal

Abstract

Solar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance. Diagnosing and resolving faults within these systems promptly is crucial to ensure reliability and efficiency in energy generation. Autoencoders and their variants have gained popularity in recent studies for detecting and diagnosing faults in solar arrays. However, traditional autoencoder models often struggle to capture the spatial and temporal relationships present in photovoltaic sensor data. This paper introduces a deep learning model that combines a graph convolutional network with a variational autoencoder to diagnose faults in solar arrays. The graph convolutional network effectively learns from spatial and temporal sensor data, significantly improving fault detection performance. We evaluated the proposed deep learning model on a recently published solar array dataset for an integrated power probability table mode. The experimental results show that the model achieves a fault detection rate exceeding 95% and outperforms the conventional autoencoder models. We also identified faulty components by analyzing the model's reconstruction error for each feature, and we validated the analysis through the Kolmogorov–Smirnov test and noise injection techniques.

Citation

ARIFEEN, M., PETROVSKI, A., HASAN, M.J., NOMAN, K., NAVID, W.U. and HARUNA, A. 2024. Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays. Machines [online], 12(12), article 894. Available from: https://doi.org/10.3390/machines12120894

• Journal Article Type: Article
• Acceptance Date: Dec 4, 2024
• Online Publication Date: Dec 6, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jan 9, 2025
• Publicly Available Date: Jan 9, 2025
• Journal: Machines
• Electronic ISSN: 2075-1702
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 12
• Article Number: 894
• DOI: https://doi.org/10.3390/machines12120894
• Keywords: Solar array; Photovoltaic array; Fault detection; Fault diagnosis; Graph convolutional network; Variational autoencoder
• Public URL: https://rgu-repository.worktribe.com/output/2619932