Anti-sine-cosine atom search optimization (ASCASO): a novel approach for parameter estimation of PV models.

Abstract

Nowadays, solar power generation has gradually become a part of electric energy sharing. How to effectively enhance the energy conversion efficiency of solar cells and components has gradually emerged as a focal point of research. This paper presents a boosted atomic search optimization (ASO) with a new anti-sine-cosine mechanism (ASCASO) to realize the parameter estimation of photovoltaic (PV) models. The anti-sine-cosine mechanism is inspired by the update principle of sine cosine algorithm (SCA) and the mutation strategy of linear population size reduction adaptive differential evolution (LSHADE). The working principle of anti-sine-cosine mechanism is to utilize two mutation formulas containing arcsine and arccosine functions to further update the position of atoms. The introduction of anti-sine-cosine mechanism achieves the populations' random handover and promotes the neighbors' information communication. For better evaluation, the proposed ASCASO is devoted to estimate parameters of three PV models of R.T.C France, one Photowat-PWP201 PV module model, and two commercial polycrystalline PV panels including STM6-40/36 and STM6-120/36 with monocrystalline cells. The proposed ASCASO is compared with nine reported comparative algorithms to assess the performance. The results of parameter estimation for different PV models of various methods demonstrate that ASCASO performs more accurately and reliably than other reported comparative methods. Thus, ASCASO can be considered a highly effective approach for accurately estimating the parameters of PV models.