Optimal design of heliostat field based on improved particle swarm optimization algorithm

An improved particle swarm optimization algorithm was proposed to solve the problem of the optimal layout of fixed-tilt mirrors in tower solar thermal power plants. This article introduced nonlinear inertia weight and asynchronous learning factor. It also provided an example to verify the method. In this paper, the rated annual average thermal power output for a fixed-tilt mirror field reached a certain size. The unit area annual average output of thermal power was taken as the evaluation criterion for optimizing the layout of the mirror field. Firstly, the initial optical efficiency and thermal power of the fixed-tilt mirror field were determined based on the initial layout position and parameters. Then, based on the characteristics of the absorption tower radiating outward, the fixed-tilt mirrors were classified from inside to outside to form a layered structure to optimize the solution dimension. Then, the improved particle swarm algorithm was used to optimize each parameter of the fixed-tilt mirror, to find the global optimal solution and obtain the optimal distribution position and parameters of the fixed-tilt mirror. Finally, the improved algorithm was compared with the standard particle swarm algorithm: from the experimental results, it can be seen that using the improved particle swarm algorithm can find a better hyperparameter for the mirror field layout model. It also proves that the improved algorithm has better convergence speed, robustness, and adaptability, and verifies the correctness and effectiveness of this improved method.