Conscious neighborhood scheme-based Laplacian barnacles mating algorithm for parameters optimization of photovoltaic single- and double-diode models

By R. M. Rizk-Allah, A. El‐Fergany. Energy Conversion and Management, 2020

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Abstract This paper presents a novel conscious neighborhood strategy-based Laplacian barnacles mating algorithm, named NLBMA for solving solar cell diode models. It is an indispensable and prudent improvement to address insufficient diversification and intensification inclinations and the entrapment in the local optima of the original barnacles mating algorithm (BMA). In this regard, the proposed NLBMA introduces two new searching methodologies called Laplacian-based crossover search (LCS) and neighborhood-based wandering search (NWS) in order to boost the searching ability of barnacles by exploring different regions within the search space. In this sense, the LCS aims to improve the diversification of solutions while the NWS operates in enhancing the intensification ability by refining the solution quality and then the balancing between local and global searches can be enhanced consciously. The proposed NLBMA is used to optimize the parameters of single-diode (SD), and double-diode (DD) models of photovoltaic (PV) units. In all experiments, NLBMA is compared with the standard BMA and different state-of-the-art recent optimization algorithms. The comprehensive and statistical results indicate that NLBMA is more accurate and superior compared to other peers, where for all the studies PV models/modules, the NLBMA has proved to consistently achieve improved results for the root mean squared error (RMSE) of current than the others. For example, the results of R M S E found by NLBMA for R.T.C. France silicon solar cell are 7.73006e−4 A for SD model and 7.5242e−4 A for DD model, with percentage of improvement 83.23% and 90.94% on the original BMA variant, respectively. For the KC200GT module, the found R M S E values are 1.9827e−3 A and 2.0083e−3 A with saving 1.48% and 96.27% for the SD and DD modules over the original BMA variant, respectively. For the Photowatt PWP-201 module, the results are 3.3610e−2 A and 3.3043e−2 A with improvement 67.19% and 73.55% for the SD and DD models compared to the original BMA variant, respectively. Therefore, the NLBMA can efficiently prove its superiority and reliability to tackle the problem of parameters’ extraction of the PV models.

Subfields: Artificial Intelligence, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/c557a8bef1ce7962d8fd44c96c817f0213de1102