# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
###### Abstract  
Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200 GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power-voltage (P-V) and current-voltage (I-V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism. Keywords:photovoltaic; parameter extraction; TFWO; optimization; double diode model; and three diode model +
Footnote †: journal: _articles  
Several advanced applications have been introduced based on PV electricity, such as feeding the required power for satellite communication [8], greenhouse cooling and heating [9], water pumping for agriculture [10; 11; 12], supplying electronic devices and indoor lighting [13; 14], etc. The PV characteristics can be analyzed with power-voltage (P-V) and current-voltage (I-V) curves. These curves are dependent on several parameters, such as incident solar irradiance, ambient temperature, and the investigated equivalent circuit of the PV model [15; 16; 17; 18]. The PV characteristics depend on different unknown parameters due to a lack of data from the PV manufacturing datasheet [19]. Improving and analyzing the performance of PV cells/modules is imperative due to their widespread applications, which require optimal extraction of the unknown parameters. These parameters are changed according to the investigated PV models which can be a single diode model (SDM), double diode model (DDM), and three diode model (TDM). Consequently, the number of unknown parameters are five, seven and nine for the SDM, DDM, and TDM, respectively. These parameters are estimated in three ways: iterative methods, machine learning, and meta-heuristic optimization algorithms [20; 21; 22; 23; 24; 25; 26]. The iterative methods have been applied to estimate the PV parameters in [27; 28; 29; 30], such as Lambert W function [27], linear least squares [28], maximum likelihood-based Newton-Raphson [29], and Gauss-Seidel [30]. On the other hand, several researchers made an assumption or neglected some parameters to reduce the number of variables required to be extracted. Lately, various optimization techniques have been carried out in the extraction of PV parameters, such as the elephant herd algorithm [31], multiple learning backtracking search algorithm [32], gray wolf optimizer, cuckoo search algorithm [33], opposition-based sine cosine approach with local search [34], logistic chaotic JAYA algorithm [35], moth-flame algorithm (MFA), orthogonal Nelder-Mead MFA [36], and improved teaching-learning-based optimization (TBLO) algorithm [37]. In [38], the MFA has been utilized for the three diode PV model considering the ideality factors for the second and third diode as added control variables.