Abstract
This paper presents the study and the use of two smart swarm-based optimization methods for determining the electrical unknown parameters of solar photovoltaic cells. These two methods are the well-known Particle Swarm Optimization (PSO) and a recent smart swarm-based method named, Whale Optimization Algorithm (WOA). This last one is inspired by the hunting behaviour of humpback whales in nature. The best parameters determination values are essential for the accuracy of the solar photovoltaic characteristics. The non-linear parameters determination problem is formulated mathematically as a multi-parameters or as a multi-objective optimization problem. The two swarm-based optimization methods are first described, explaining every step, and then validated using solar photovoltaic manufacturers’ data sheets information. The performance of each approach is evaluated in terms of chosen criteria. The results show that the WOA method outperforms the PSO.
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Tchoketch Kebir, S., Ait Cheikh, M.S., Haddadi, M. (2019). A Set of Smart Swarm-Based Optimization Algorithms Applied for Determining Solar Photovoltaic Cell’s Parameters. In: Hatti, M. (eds) Renewable Energy for Smart and Sustainable Cities. ICAIRES 2018. Lecture Notes in Networks and Systems, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-030-04789-4_42
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