Article Open Access

Computer Vision for Monitoring Renewable Energy Infrastructure

Ahmed Ali Hussein, Sumaia Ali Alal, Saad Abdulaziz Abdulrahman, Hanaa Hameed Merzah, Hasan Ali Abbas, M. Batumalay

Abstract


The operational efficiency of renewable energy installations, including solar, wind, and hydropower systems, is often hindered by the limitations of manual inspections and legacy monitoring. These methods lack the real-time, scalable fault detection necessary to prevent costly downtime. This paper proposes a comprehensive computer vision framework for automated fault detection, predictive maintenance, and inspection optimization across diverse renewable energy infrastructures. We developed a hybrid deep learning model, based on ResNet-50 with attention-based extensions, to analyze high-resolution imagery from drones and stationary cameras. The model was trained and validated on a dataset of 20,000 labeled images covering infrastructure-specific defects such as photovoltaic microcracks, wind turbine blade erosion, and hydropower sedimentation patterns. Our experiments demonstrate high-performance, with fault detection accuracy exceeding 91% for all categories and inference latencies under 70ms. The system significantly improved predictive maintenance outcomes, reducing unplanned outages by over 77% and decreasing inspection energy consumption by more than 70%. Scalability tests on a larger 50,000-image dataset confirmed the framework's robustness, maintaining high accuracy and processing speed. This work validates computer vision as a viable, cost-effective, and scalable solution for intelligent monitoring in the renewable energy sector, offering significant practical implications for autonomous diagnostic systems in smart grid and industrial applications for energy efficiency.


Keywords


Computer Vision, Renewable Energy Monitoring, Energy Efficiency, Fault Detection, Predictive Maintenance, Deep Learning

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DOI: https://doi.org/10.52088/ijesty.v5i1.1727

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Copyright (c) 2025 Ahmed Ali Hussein, Sumaia Ali Alal, Saad Abdulaziz Abdulrahman, Hanaa Hameed Merzah, Hasan Ali Abbas, M. Batumalay

International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674