Artificial Intelligence-Driven Monitoring and Predictive Maintenance in Floating Solar Power Plants: A Comprehensive Review
Keywords:
Artificial Intelligence, Floating Solar Power Plants, Predictive Maintenance, Machine Learning, Fault Detection, IoT Monitoring, Renewable Energy SystemsAbstract
Floating solar power plants (FSPPs), an emerging sustainable alternative in renewable power generation, can apply best in regions that lack availability of land into which solar has been typically installed. By mounting the panels over water bodies such as reservoirs, lakes, and dams, floating solar consists of several benefits like increased efficiency due to cooling features for the water, reduced evaporative loss for the water, and optimal use for exploited water surfaces. But alongside these positive effects are hurdles in the operation and maintenance of the floating solar installations. These include: structural instability, panel degradation, corrosion, electrical breakdowns, and difficulty in conducting regular maintenance checks on water surfaces. Artificial Intelligence (AI) has gained amazing eminence lately as a robust technology for the optimization of monitoring and predictive maintenance in renewable energy systems. This domain thrives on harnessing AI-driven techniques for analysing vast volumes of operational and environmental data, which in return raises the possibility of anomaly detection, performance degradation identification, and prediction of possible equipment failures before they actually do. This analytical review will take a look at the recent developments and research trends applicable in AI-based monitoring and predictive maintenance in floating solar power plants. The paper gives an overview of various approaches viz., machine-learning algorithms, deep-learning models, computer-vision techniques, and the IoT-based smart monitoring system that can be used to assess the performance and detect a fault in real-time. The review not only presents a critical examination of the technique's strength and weakness but also discusses the problems of data availability, environmental variability, and system scalability. The review also points out the research gaps and future areas of research for integrating the latest AI frameworks with floating solar technology. In summary, these AI-powered monitoring systems do have the potential to help improve productivity, improve operation reliability, and decrease maintenance costs in the management of floating solar power plants.
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