Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Abstract

In this paper, an enhanced social network search algorithm (ESNSA) has been proposed to model the solar photovoltaic (PV) modules accurately and efficiently. The proposed algorithm is introduced to minimize the least root-mean-square error (RMSE) between the calculated and experimental data for the single, double, and triple diode models of Kyocera KC200GT, STM6(40/36), and Photowatt-PWP201 modules. The original SNSA was inspired by users on social networks and their many moods, including imitation, conversation, disputation, and innovation mood. Two strategies are presented for the ESNSA. The first strategy is the powerful exploitation strategy (PES), which is intended to increase the SNSA's performance by boosting searching around the best view of all users. The second strategy is to suggest an adaptable parameter to aid in the exploitation of iterations in the end. Diverse comparisons and statistical analyses for validation purposes are carried out for mono-crystalline STM6(40/36), multicrystalline KC200GT, and polycrystalline photowatt-PWP201 modules. The comparative studies and statistical measures show the consistency and accurateness of the proposed ESNSA. As a numerical application, for the mono-crystalline STM6(40/36) PV module, the proposed ESNSA achieves the least RMSE of 1.751631E−3, 1.769953E−3, and 1.696504E−3, respectively for the three models. Also, it shows high robustness compared to the original SNSA as it acquires the least standard errors for the three models of 2.56E−18, 1.76E−6, and 1.24E−5, respectively. Moreover, the proposed ESNSA provides a higher convergence speed where it is approximately reached to the least RMSE in less than 60%, 50%, and 60% of the iterations for the three models, respectively. Nevertheless, the proposed ESNSA provides better performance than miscellaneous published approaches in minimizing the RMSE, with high robust indices.

Hybrid approach for parameter identification of the two-diode model of photovoltaic modules

“…Up to now, intelligent algorithms have been widely used in the modeling of PV modules, including differential evolution with reinforcement learning (Hu et al , 2021), modified teaching–learning-based optimization (MTLBO) (Abdel-Basset et al , 2021), fractional chaotic ensemble particle swarm optimizer (Yousri et al , 2020), slime mold algorithm (Mostafa et al , 2020), slap swarm algorithm (Messaoud, 2020), teaching–learning-based artificial bee colony (TLABC) (Chen et al , 2018), enhanced leader particle swarm optimization (ELPSO) (Rezaee Jordehi, 2018), grey wolf optimizer and cuckoo search (GWOCS) (Long et al , 2020), improved whale optimization algorithm (Xiong et al , 2018), classified perturbation mutation based particle swarm optimization (CPMPSO) (Liang et al , 2020), guaranteed convergence particle swarm optimization (GCPSO) (Nunes et al , 2018), performance-guided JAYA (PGJAYA) (Yu et al , 2019), enhanced social network search algorithm (ESNSA) (Shaheen et al , 2022), hybrid methodology named WDOWOAPSO that combines diversification and intensification mechanisms from wind driven optimization, whale optimization algorithm and particle swarm optimization (PSO) (Nunes et al , 2019), forensic-based investigation algorithm (FBIA) (Shaheen et al , 2020) and improved teaching–learning-based optimization (Li et al , 2019). This is due to their universal global search capabilities and their effectiveness in dealing with nonlinear functions, resulting in rapid computation and high precision.…”

“…and accuracy. To investigate the precision of the proposed method, it is compared with several well-established algorithms, that is, TSLLS, modified TSLLS, RF, modified RF, MTLBO, CPMPSO, GWOCS, GCPSO, TLABC, ELPSO, PGJAYA, ESNSA, social network search algorithm (SNSA)(Shaheen et al, 2022), WDOWOAPSO and FBIA. Parameter extraction for the single-diode model and the two-diode model are both involved.…”