Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer

Abstract

The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters estimation using optimization algorithms is a challenging problem in which a wide range of research has been conducted. The idea behind this challenge is the selection of a proper PV model and algorithm to estimate the accurate parameters of this model. In this paper, a new application of the improved gray wolf optimizer (I-GWO) is proposed to estimate the parameters’ values that achieve an accurate PV three diode model (TDM) in a perfect and robust manner. The PV TDM is developed to represent the effect of grain boundaries and large leakage current in the PV system. I-GWO is developed with the aim of improving population, exploration and exploitation balance and convergence of the original GWO. The performance of I-GWO is compared with other well-known optimization algorithms. I-GWO is evaluated through two different applications. In the first application, the real data from RTC furnace is applied and in the second one, the real data of PTW polycrystalline PV panel is applied. The results are compared with different evaluation factors (root mean square error (RMSE), current absolute error and statistical analysis for multiple independent runs). I-GWO achieved the lowest RMSE values in comparison with other algorithms. The RMSE values for the two applications are 0.00098331 and 0.0024276, respectively. Based on quantitative and qualitative performance evaluation, it can be concluded that the estimated parameters of TDM by I-GWO are more accurate than those obtained by other studied optimization algorithms.

Parameter Estimation of Modified Double-Diode and Triple-Diode Photovoltaic Models Based on Wild Horse Optimizer

“…This application focused on the comparison of the WHO with other algorithms. A TDM was selected for this comparison, and the real data of the Photowatt-PWP201 PV module was applied [28,29]. Photowatt-PWP201 PV module contains 36 polycrystalline silicon cells connected in series and operating at an irradiance of 1000 W/m 2 and temperature of 45C.…”

“…Photowatt-PWP201 PV module contains 36 polycrystalline silicon cells connected in series and operating at an irradiance of 1000 W/m 2 and temperature of 45C. The accuracy of the WHO's estimated parameters was tested through the best RMSE and compared with recent robust algorithms in literature (I-GWO, CGO, HBO) [28,29]. A comparison between the obtained Root Mean Square Errors (RMSE) values for all compared algorithms is presented in Table 5.…”

“…This survey included Teaching-Learning-Based Optimization (TLBO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Simulated Annealing (SA), and Flower Pollination Algorithm (FPA). Recent different algorithms in literature include a proposed Turbulent Flow of Water Optimizer (TFWO) for estimating the optimal values for different PV models [26], a proposal to optimize the parameters of DDM and SDM by an improvement of the equilibrium optimizer using Linear Reduction Diversity technique (LRD) and local Minima Elimination Method (MEM) [27], and a parameters estimation for TDM using Chaos Game Optimization algorithm (CGO) and Improved Grey Wolf Optimizer (I-GWO) by [28] and [29], respectively. The algorithms' behavior in the two references was tested through different evaluation methods.…”

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

“…The DDM has two diodes, one series resistance, and one parallel resistance; the second diode represents the recombination effect in the P-N junction [11][12][13][14]. The TDM has three diodes, one series resistance, and one parallel resistance; the third diode represents the effect of leakage current and grain boundaries [15][16][17]. The total estimated parameters for the SDM, DDM, and TDM are five, seven, and nine parameters, respectively.…”

Salp Swarm Optimization Algorithm for Estimating the Parameters of Photovoltaic Panels Based on the Three-Diode Model

“…Likewise, their analysis of the resulting errors in the V-I or V-P profiles was not clear. Other techniques were also applied in [23][24][25][26][27][28]: the tree growth algorithm (TGA), dynamic self-adaptive and mutual-comparison teaching-learning-based optimization, the moth-flame optimization algorithm, improved gray wolf optimization, and the SSA. These implementations, as the previous ones, are commonly used for cells or PV modules, where restrictions to the search space are evident, without convincing statistical results or tuning the optimization techniques to be able to determine the minimization of the objective function in the best conditions of each optimization algorithm.…”