High-concentration photovoltaic (HCPV) systems require ultra-precise sun-tracking to maximize solar energy concentration and reduce optical losses. Achieving such precision while maintaining low energy consumption and simplicity of implementation remains a major challenge. This study addresses this issue by proposing a more accurate and efficient sun-tracking strategy.
The proposed control method is based on genetic algorithms (GA), which dynamically optimize the alignment of the tracker’s normal vector with the sun’s position. A customized fitness function was designed to guide this optimization process by integrating both the current solar coordinates and the previous orientation of the tracker. The strategy was evaluated using a high-fidelity Simulink model replicating real-world HCPV operational conditions and benchmarked against open-loop and particle swarm optimization approaches.
The simulation results show that the system consistently maintains a tracking precision of 0.1°, a level of accuracy that is not reported in CPV applications. In addition, the method significantly reduces actuator energy consumption, enhancing operational efficiency and decreasing mechanical stress over time.
The proposed strategy was validated through detailed simulations that closely replicate real-world conditions. While the results are promising in terms of tracking accuracy and energy efficiency, further experimental validation will enhance confidence in its applicability to commercial CPV systems. Future investigations may explore integration with hardware platforms and adaptive improvements under dynamic environmental scenarios to fully exploit the method’s potential in operational settings.
The proposed GA-based sun-tracking strategy offers a practical solution for enhancing the performance of CPV systems. By achieving high tracking accuracy without relying on external sensors, the method simplifies system design and reduces installation and maintenance costs. Its ability to minimize actuator energy consumption also extends component lifespan and improves overall system efficiency. This approach is especially suitable for applications requiring precise alignment, such as rooftop CPV installations or remote areas where energy efficiency and reliability are critical. With further validation, it can be directly implemented in real-world systems to support cost-effective solar energy harvesting.
This work introduces a novel, real-time sun-tracking strategy for HCPV systems that outperforms traditional techniques in both accuracy and energy efficiency. By eliminating the need for complex sensor setups and minimizing power usage, the proposed approach represents a scalable and cost-effective solution to improve the viability and performance of future HCPV installations.
