review · Sensors
A review of research published between 2018 and 2023 outlines recent developments in predictive maintenance and cybersecurity for solar panel installations. Modern methods increasingly combine the Internet of Things, machine learning, and deep learning to support real-time monitoring, operational data analysis, and anomaly detection. These approaches assist in identifying panel defects, general degradation, and operational irregularities, allowing earlier interventions that reduce the likelihood of inverter failures. However, deploying predictive maintenance within the renewable energy sector faces distinct operational hurdles. Key challenges include finding an effective balance between analytical model complexity and accuracy, coping with inherent system unpredictability, and adjusting systems to varying environmental conditions. Overcoming these constraints is essential for maintaining accurate, effective, and secure solar plant maintenance procedures.
Solar power systems must operate reliably to deliver steady renewable energy. Unplanned equipment breakdowns, such as inverter failures, disrupt electricity generation and increase operating costs. Understanding how advanced digital tools and predictive maintenance detect faults early helps keep solar plants functional and resilient against operational disruptions and environmental changes.
The findings apply to solar energy operators and maintenance service providers seeking to improve fault detection through Internet of Things and machine learning tools. Because the evidence derives from a review of research between 2018 and 2023 rather than a validated commercial product, the field remains largely in the applied research and testing phase as developers work to overcome environmental variability and model complexity.
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This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures. The analysis also emphasizes how challenging it is to adopt predictive maintenance in the renewable energy industry. Achieving a balance between model complexity and accuracy, dealing with system unpredictability, and adjusting to shifting environmental conditions are among the challenges. It also highlights the Internet of Things (IoT), machine learning (ML), and deep learning (DL), which are all incorporated into solar panel predictive maintenance. By enabling real-time monitoring, data analysis, and anomaly identification, these developments improve the accuracy and effectiveness of maintenance procedures.
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DOI: 10.3390/s25010206
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