Three-diode Model Research Articles

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

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 estimating the unidentified parameters of PV 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.

An interval branch and bound global optimization algorithm for parameter estimation of three photovoltaic models

In this paper, an interval branch and bound algorithm is proposed to estimate the parameters for three models of photovoltaic (PV) cells. These models are a single diode model (SDM), a double diode model (DDM) and a three diode model (TDM). The minimization of Maximal Absolute Error (MAE) based on the infinite norm is evaluated and compared with the common used objective function called the Root Mean Square Error (RMSE). The MAE aims to minimize the average error on a set of experimental data. In this regard, the parameter estimation is reformatted. The performance of the proposed optimization algorithm is analyzed and tested on all three models. It has good convergence and robust statistical analysis at different operating conditions as well as for various models. The estimated performance characteristics for current-voltage (I-V) and power-voltage (P-V) of the tested cells are very close to the experimental data and compete with existing works in the literature.

Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm

This article proposes an accurate and straightforward method for modeling and simulation of photovoltaic (PV) modules. The main target is to find the nine-parameter of a three-diode (TD) model based on the datasheet parameters, which are given by all commercial PV modules. The objective function is formulated based on short circuit, open circuit, power derivative, and maximum power equations. Two parameters (parallel resistance and photo-generated current) are calculated analytically and rest parameters are optimally designed using the sunflower optimization (SFO) algorithm. The presented method is applied to model three types of commercial PV modules (multicrystal KC200GT, poly-crystalline MSX-60, and mono-crystalline CS6K-280M). The optimal nine-parameters obtained in this paper are paralleled with that attained by other approaches. In order to assess the efficiency of the offered approach, I-V and P-V characteristics are validated with measured data under various temperatures and solar irradiations. The error among these results records a value less than 0.5%. Therefore, the simulation results indicate an excellent agreement with the measured data. This proposed approach can be utilized to model any marketable PV module based on given datasheet parameters only.

Comprehensive three-diode model of photovoltaic array with partial shading capability

Comprehensive three-diode model of photovoltaic array with partial shading capability

Method of Extracting Solar Cell Parameters From Derivatives of Dark I–V Curves

A method is presented to extract solar cell parameters from the derivatives of dark current–voltage curves with a three-diode model, using the monotonic properties of the current–voltage characteristic associated with each diode or resistive current. The method yields an improved accuracy of the solar cell parameters when compared with that used on the actual current–voltage curves. Despite the complexity of the three-diode model with seven fit parameters, a good accuracy can be reached using the proposed method with a relatively small computational effort. A hypothetical case is presented using various approaches to obtain the solar cell parameters, with a root-mean-square error for the logarithm of the current–voltage curve ( $\text{RMS}_{\text{log}_{10}I}$ ) of $4{\,\,\times\, 10}^{-3}$ . An example fitting is then demonstrated on a multicrystalline passivated emitter rear contact solar cell, yielding $\text{RMS}_{\text{log}_{10}I}=1.45{\,\,\times\, 10}^{-1}$ .

Comprehensive Three-diode Model of Photovoltaic Array with Partial Shading Capability

In this paper, we present a new approach of comprehensive MATLAB/Simulink simulator for photovoltaic generator based on a three-diode equivalent circuit, taking in consideration seven parameters and it needs only the parameters presented in the data sheet by the manufacturer. For characterisation of any photovoltaic generator, the simulator supports a large array combination with high accuracy. The results obtained by the simulator are compared with previously published simulator and the experimental data. The proposed work can be very useful for photovoltaic (PV) professionals and developers that can be interfaced with different kind of maximum power point tracking (MPPT) algorithms and power electronic converters, and its present capability with partial shading conditions (PSC).

Effect of various model parameters on solar photovoltaic cell simulation: a SPICE analysis

In this paper, all the models of PV cell, namely ideal single-diode model, single-diode $$R_{\rm s}$$ model, single-diode $$R_{\rm p}$$ model, the two-diode model, and the three-diode model, have been discussed. SPICE simulation is done to evaluate the impact of model parameters on the operation of PV cell. The effects of the parameters are discussed. The photocurrent, $$I_{\rm L},$$ is proportional to irradiance, and the series resistance, $$R_{\rm s},$$ reduces the short-circuit current and fill factor. The parallel resistance, $$R_{\rm p},$$ reduces the open-circuit voltage, and both the diffusion diode and recombination diode reduce the open-circuit voltage value and fill factor. Finally, it is shown that an increase in cell operating temperature reduces the open-circuit voltage and fill factor and thus degrades the performance significantly.

Study of Nickel Silicide Formation and Associated Fill-Factor Loss Analysis for Silicon Solar Cells With Plated Ni-Cu Based Metallization

In this study, the impact of impurities incorporated into plated nickel seed layer on silicide formation and the influence of annealing temperature on the fill-factor (FF) loss of solar cells with Ni–Cu contacts is investigated. The silicide growth of electroless plated nickel seed layer is significantly retarded compared with literature data on physical-vapor-deposition (PVD)-based nickel annealed at 550 °C. X-ray photoelectron spectroscopy and X-ray diffraction reveal the presence of SiO2 at the Ni–Si interface and the formation of nickel phosphides in addition to nickel silicide. The retardation in silicide growth is attributed to the presence of (111) planes after the texturing process and contaminants in the seed layer. Varying the annealing temperature of fabricated cells from 350 °C to 425 °C led to a decrease in the average FF from 79.3% to 77.5%. The loss analysis is based on Suns– V oc measurements, illuminated current–voltage parameters, and dark current–voltage curve fitting based on a three-diode model. It reveals that the FF loss is dominated by increased junction recombination, whereas losses due to third-diode component become significant for annealing at 400 °C and higher temperatures. The results highlight the need to carefully tune the seed layer annealing parameters to the interface conditions and junction depth of solar cells.

Tunable open-circuit voltage in ternary organic solar cells

Recently, there are growing interests in ternary organic solar cells due to their expected broader absorption in comparison to their counterpart binary systems. It is found that open-circuit voltage in a ternary system can be largely tuned by varying the composition ratio of two donors or two acceptors integrated in. However, there is no detailed analysis for such an interesting observation. In this paper, we fabricated ternary solar cells with one p-type polymer as donor and two mixed n-type fullerenes as acceptor, and investigated the open-circuit voltage by varying the weight ratio of the two fullerenes. A three-diode model is developed to analyze the tunable behavior of open-circuit voltage displayed in our ternary system in terms of energy loss and dark current suppression. We also discussed the tunable open-circuit voltage based on electron and hole quasi-Fermi levels.

MATLAB Modeling and Simulation of Photovoltaic Modules

In this paper a modeling method is investigated that finds the non-linear equation parameters of a photovoltaic (PV) module in order to obtain the desired PV model using any circuit simulator. This modeling method adjusts the I-V curve at three remarkable points: the open circuit voltage, the short circuit current, and the maximum power point [1]. Three models are realized using this technique namely, the single-diode model, the two-diode model, and the three-diode model. The evaluation study of the accuracy of these three models showed relative errors ranging from 32% to 50%. Further, this technique is improved by adjusting the I-V curve at more than three points depending on the number of unknowns to be found for each model, which showed a reduction in the relative error ranging from 0.37% to 38%.