Solar Tracking Control Algorithm Based on Artificial Intelligence Applied to Large-Scale Bifacial Photovoltaic Power...

Area of Science:

• Renewable Energy
• Artificial Intelligence
• Solar Power Optimization

Background:

• The global shift to a low-carbon economy necessitates advancements in solar energy technologies.
• Solar trackers enhance photovoltaic (PV) plant capacity by following the sun's path.
• Optimizing solar tracker performance requires accounting for variables like panel spacing, reflectivity, bifacial panels, and climate fluctuations.

Purpose of the Study:

• To develop and validate an artificial intelligence (AI)-based algorithm for solar trackers.
• To integrate key environmental and operational factors, including weather variations and inter-panel distance, into solar tracking.
• To improve the energy generation efficiency of photovoltaic systems.

Main Methods:

• An AI-based algorithm was designed to dynamically adjust solar tracker positioning.
• The algorithm incorporates real-time weather data and optimizes spacing between solar panels.
• Effectiveness was validated using bifacial panels in a real-world solar plant in northeastern Brazil.

Main Results:

• The AI algorithm achieved energy gains of up to 7.83% on cloudy days.
• An average energy gain of approximately 1.2% was observed compared to a commercial solar tracker.
• The developed methodology proved replicable globally.

Conclusions:

• AI-driven solar tracking algorithms can significantly enhance energy yield.
• Accounting for dynamic factors like weather and panel proximity is crucial for maximizing PV plant efficiency.
• The proposed algorithm offers a robust solution for optimizing solar energy generation worldwide.