Design and implementation of FPGA-based Taguchi-chaos-PSO sun tracking systems

Abstract

The field-programmable gate array (FPGA) based intelligent sun tracking system proposed in this paper uses an NI 9642 controller to integrate the dual-axis sun tracking system with a Maximum Power Point Tracker (MPPT), so as to effectively increase the output power of solar panels. Furthermore, it is provided with multiple intelligent functions, so that the system can start up the sun tracking function automatically in the daytime, and automatically return to its initial position at night. It has a delay function to reduce the electric power consumed by the motor in rotation. Moreover, it can be switched to dual-axis or one-axis sun tracking freely as required by the user, and the solar panel inclination can be operated directly. The dual-axis sun tracking system uses the Particle Swarm Optimization (PSO) method to look for the parameters of the PI controller. The Taguchi Method and Logistic Map are proposed to enhance the steady state convergence of PSO in seeking the optimal solution. The MPPT uses Fuzzy Logic to adjust the step length of the incremental conductance method, so as to remedy the defects in the traditional fixed step method, and to make the solar panel output reach the maximum power point position rapidly and stably.