Optimal Selection of the Control Strategy for Dual-Axis Solar Tracking Systems

This article proposes a methodology for the optimal selection of the control strategy for two-axis solar tracking systems, that simultaneously reduces tracking error and energy consumption in existing solar tracking systems, improving their overall performance. Begins with the characterization of the tracking system, then the constraint definition helps to pre-select the possible controllers. Subsequently, a selection stage is carried out from a heuristic approach, based on a multibody simulation analysis and an experimental analysis, and the feasible controllers for the application of the tracker are defined. Finally, a comparative analysis is carried out to find the best control strategy for the existing tracker and the solar application. The optimal selection approach was implemented in a solar tracking system for low-power photovoltaic applications. Based on the defined constraints, six control strategies were pre-selected, which were simulated and implemented in the physical system. The multibody simulation process allows the designer to know the dynamics of each controller, and in turn determine an approximation of the best configuration of the elements that compose it. This, with the aim of validating that each proposal is compatible with the application of the solar tracker, and that it can be taken to the real experimental environment. From the experimental results, the MPC controller shows the best performance. Well, although it has a greater error than other alternatives, its value is still below the permissible precision level (less than 2°) and in turn has the lowest energy consumption 0.7867 Wh. That is, a reduction ranging from 34 to 76% with respect to each performance of the alternatives considered. In addition, the dynamics of the control actions it performs are smoother, thus reducing wear on the actuators.Thus, the results obtained to support that with the proposed methodology the overall performance of solar tracking systems can be increased, significantly reducing tracking error and energy consumption.