Parameter estimation of photovoltaic models using an improved marine predators algorithm. (1st January 2021)

• Record Type: Journal Article
• Authors: Abdel-Basset, Mohamed; El-Shahat, Doaa; Chakrabortty, Ripon K.; Ryan, Michael
• Is Part Of: Energy conversion and management. Volume 227(2021)
• Publication Date: 2021-01-01
• Subjects: Parameter estimation -- Photovoltaic model -- Marine predators algorithm -- Solar energy; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113491
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Marine Predators Algorithm (MPA) was used for the first time for PV models. An Improved MPA was proposed to accurately estimate the parameters of different PV models. High-quality solutions were refined using an adaptive mutation operation. The proposed algorithm was compared with recent state-of-the-art algorithms. The superior performance of the proposed algorithm was proved in the experiments. Abstract: The abundance of solar energy as one of the clean energy forms offers a great advantage as an alternative to non-renewable energy sources. The photovoltaic system is a promising technology that directly converts sunlight into a direct current. Parameter estimation of photovoltaic systems is a challenging task that has a significant influence on the efficiency of these systems. Most of the existing methods employed for identifying parameters of photovoltaic systems suffer from high computing burdens, fall into local optima, or struggle with the intricate adjustment required of the algorithm parameters to provide the best performance. This paper, therefore, proposes an improved algorithm based on the new metaheuristic marine predators algorithm to extract the optimal values of photovoltaic parameters. The improved marine predators algorithm employs a population improvement strategy to enhance the quality of the solutions by utilizing two different ways to handle the solutions inside the population-based on the population mean fitness. The location of a high-quality solution is improved using an adaptive mutation operation, while the location of a low-quality solution is updated according to the location of the best-obtained solution and the location of a good solution selected from the population. A good solution is chosen from the first half of the population after sorting its solutions in ascending order. The results of several experiments show the superior performance of the proposed algorithm compared to existing algorithms on a range of photovoltaic models. The results show that the proposed algorithm is highly correlated with the measured current–voltage data so that it can offer a useful alternative for parameter estimation of photovoltaic models.