Enhanced marine predators algorithm for identifying static and dynamic photovoltaic models parameters

Mohamed Abd Elaziz, Sudhakar Babu Thanikanti*, Ibrahim Anwar Ibrahim, Songfeng Lu, Benedetto Nastasi, Majed A. Alotaibi, Md. Alamgir Hossain, Dalia Yousri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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 algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563.

Original languageEnglish
Article number113971
Pages (from-to)1-15
Number of pages15
JournalEnergy Conversion and Management
Volume236
DOIs
Publication statusPublished - 15 May 2021

Keywords

  • Solar energy technology
  • Marine predator algorithm
  • Single diode model
  • Two diode model

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