Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Computer Science

Background:

• Solar panel systems require robust maintenance strategies to ensure optimal performance and longevity.
• Increasing complexity and data generation necessitate advanced monitoring and diagnostic techniques.
• Cybersecurity is a growing concern for interconnected renewable energy infrastructure.

Purpose of the Study:

• To systematically review recent advancements (2018-2023) in predictive maintenance for solar panel systems.
• To analyze the advantages and challenges of emerging predictive maintenance technologies.
• To examine the integration of cybersecurity measures within solar plant maintenance frameworks.

Main Methods:

• Systematic literature review of research studies, reviews, and empirical analyses from 2018 to 2023.
• Analysis of technologies including the Internet of Things (IoT), machine learning (ML), and deep learning (DL).
• Evaluation of defect detection, degradation analysis, and anomaly identification in solar panels.

Main Results:

• Predictive maintenance technologies effectively detect defects and degradation, reducing inverter failures.
• Internet of Things (IoT), machine learning (ML), and deep learning (DL) enhance real-time monitoring and anomaly identification.
• Key challenges include balancing model complexity and accuracy, managing system unpredictability, and adapting to environmental changes.

Conclusions:

• Advanced predictive maintenance, leveraging IoT, ML, and DL, significantly improves solar panel system efficiency and reliability.
• Addressing challenges in adoption is crucial for widespread implementation in the renewable energy sector.
• Integrated cybersecurity is essential for secure and effective solar plant operations.