Abstract
Solar energy is used worldwide to alleviate the daily increasing demands for electric power. Photovoltaic (PV) cells, which are used to convert solar energy into electricity, can be represented as equivalent circuit models, in which a series of electrical parameters must be identified in order to determine their operating characteristics under different test conditions. Intelligent approaches, like those based in population-based optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Simulated Annealing (SA), have been demonstrated to be powerful methods for the accurate identification of such parameters. Recently, chaos theory have been highlighted as a promising alternative to increase the performance of such approaches; as a result, several chaos-based optimization methods have been devised to solve many different and complex engineering problems. In this paper, the Chaotic Gravitational Search Algorithm (CGSA) is proposed to solve the problem of accurate PV cell parameter estimation. To prove the feasibility of the proposed approach, a series of comparative experiments against other similar parameters extraction methods were performed. As shown by our experimental results, our proposed approach outperforms all other methods compared in this work, and proves to be an excellent alternative to tackle the challenging problem of solar cell parameters identification.
Highlights
In recent years, several economic and environmental phenomenon, such as the non-stopping increase on the cost of fossil fuel along with its probable depletion in the near future, the dramatic increase on the air pollution, and the ever worrying climatic changes and global warming effect, have motivated an increasing trend on the use of renewable energy sources [1].Solar energy is one of the most practical alternative energy sources, with it being used worldwide to alleviate the daily increasing demands for electric power [2]
The Gravitational Search Algorithm (GSA) is a population-based optimization algorithm, in which, the movement of search agents within a feasible search space is guided by a set of unique evolutionary operators based in both the laws of gravitation and motion [19]
We included two different sets of experimental results: First, in Section 5.1 we show simulation results corresponding to the implementation of our proposed Chaotic Gravitational Search Algorithm (CGSA)-based solar cell parameter identification approach, in which, we study the effects of incorporating several different types of chaotic map functions into the gravitational constant of CGSA
Summary
Several economic and environmental phenomenon, such as the non-stopping increase on the cost of fossil fuel along with its probable depletion in the near future, the dramatic increase on the air pollution, and the ever worrying climatic changes and global warming effect, have motivated an increasing trend on the use of renewable energy sources [1]. Many of these evolutionary optimization methods have been reported in the literature as viable alternatives to solve the all-important problem of PV cell parameter identification [2,9,10,11,12] Some of these methods include well-known state-of-the art approaches such as Particle Swarm Optimization (PSO) [9,13], Artificial Bee Colony (ABC) [2,14], Genetic Algorithms (GAs) [11,15], Differential Evolution (DE) [10,16], Simulated Annealing (SA) [17], Harmony Search (HS) [11,18], Gravitational Search Algorithm (GSA) [19,20], among others.
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