Swarm-based nature-inspired algorithm and genetic algorithms for optimizing a sun tracker trajectory Academic Article in Scopus uri icon

abstract

  • © 2016 Taylor & Francis.Over the last 10 years, the popularity of solar panels for catching solar energy has reduced development and manufacturing costs. Nevertheless, costs per watt are still high when compared to other less-clean energy sources such as wind energy. Therefore, the goal of the sun tracker is to maximize the energy generation of solar cells, thus giving a competitive advantage to solar energy. However, finding the optimal position is a very complex task and different algorithms such as genetic algorithms or swarm-based optimization algorithms have been used to improve the results. This article shows the design and implementation of two optimal sun tracker algorithms. The first method presented is genetic algorithms, which allow finding the position of the sun tracker based on an offline solution. When genetic algorithms find the solution offline, the results can be programmed in a simple lookup table. This approximation decreases the computational cost, and it is effective for geographical climes where conditions are constant. However, there are places with nonconstant climate conditions that need online optization algorithms. In this case, a newly developed intelligent water drop algorithm is proposed for running an online solution. Both methods were designed for the sun tracker problem and were implemented. The power and energy analytics show that the algorithms increase the efficiency of the sun tracker, compared to a static solar cell, by at least 40% in some cases. The sun tracker presented gives an excellent solution for obtaining energy from the sun during diverse weather conditions. This work also introduces a novel derivation of the intelligent water drop algorithm for sun trackers based on a nonconventional trajectory and conventional genetic algorithms adjusted for sun tracker needs. The experimental results are shown in order to validate the methodologies proposed.

publication date

  • February 7, 2016