Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters

• Published in: Energy conversion and management 2021-05, Vol.236, p.113971, Article 113971
• Main Authors: Abd Elaziz, Mohamed, Thanikanti, Sudhakar Babu, Ibrahim, Ibrahim Anwar, Lu, Songfeng, Nastasi, Benedetto, Alotaibi, Majed A., Hossain, Md Alamgir, Yousri, Dalia
• Format: Article
• Language: English
• Subjects: Algorithms; Case studies; Evolutionary computation; Machine learning; Marine predator algorithm; Mathematical models; Model testing; Optimization; Parameter identification; Parameters estimation; Particle swarm optimization; Photovoltaic cells; Photovoltaics; Predators; Root-mean-square errors; Single diode model; Solar energy technology; Systems design; Two diode model
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2021.113971

•A novel enhanced MPA algorithm has been proposed for effective PV modelling.•Static and dynamic PV model parameters have been estimated.•The estimated parameters have been validated via experimental data-sets.•The statistical analysis has been performed for effectiveness of the EMPA method.•The results confirm the EMPA efficiency comparing with state-of-the-art algorithms. Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505±1.0971e-17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505±9.6235e-14 and 0.0084505±2.5235e-9). The results show the proposed model’s superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed