Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules

Abstract

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for identifying the values of the unknown parameters of various solar cells and PV modules based on different diode models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer's PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters' identification of PV of solar cells.

Review on parameter estimation techniques of solar photovoltaic systems

“…UPLO and LOUP values in calculating the bounds required objective function enhanced its estimating capability with faster convergence. Proposed coyote optimization algorithm (COA), 57 improved differential evolutionary algorithm (IDEA), 188 and tree growth algorithm (TGA) 189 was proposed to evaluate the parameters of SDM, DDM, and TDMs and considered RMSE as a efficiency-defining factor. The COA compared its results with ABC, STBLO, OBWOA and the RMSE attained with SDM, DDM, and TDM in COA method were 7.7547 Â 10 À 4, 7.64801 Â 10 À 4, and 7.59756 Â 10 À 4, respectively; wherein the TGA algorithm produced 9.280171173 Â 10 À 04,…”

A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

“…In comparison with DDM, the PV module consumes more execution time as it is required to extract more parameters than DDM. The difference between the cost functions in the two models is relatively small [47]. In the following subsections, the equivalent circuit model of SDM, DDM, and PV module models will be shown.…”

A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models

“…A triple-phase teaching-learning-based optimization (TPTLBO) [24], Coyote Optimization Algorithm (COA) [25], an interval branch and bound algorithm [26] Tree Growth Algorithm (TGA) [27], are applied to extract the parameters of different PV models of the three models. shuffled complex evolution (SCE) [28] technique was developed for only extracting the intrinsic parameters of the PVTDM.…”