Mountain Gazelle Optimiser-based single, double, and triple diode models associated solar cells and panels parameters extraction

This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves the use of different algorithms in order to extract and improve these parameters from the single diode model (SDM), double diode model (DDM) and three diode model (TDM) respectively. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will also equate the results of datasheet values from PV manufactures with experiment values obtained from PV module measurements. The meta-heuristics techniques to be considered include genetic algorithm (GA), particle swarm optimisation (PSO), harmony search (HS), flower pollination algorithm (FPA), simulated annealing (SA), teaching learning based optimisation (TLBO), and other different hybrid solutions to optimize the convergence speed. Root mean square error (RMSE) is used as a performance indicator of each meta-heuristic technique. These optimisation techniques are utilised in extracting the parameters of a 5 W polycrystalline panel at standard testing conditions. The results presented in this paper compared the performances of the mentioned meta-heuristics on the single, double and triple diode models respectively.

Perovskite solar cells (PSCs) are third-generation photovoltaic technology that has gained a lot of attention due to its technological and economic advantages. The PSCs are characterized with low cost, high absorption, long carrier diffusion length, and low fabrication temperature. To model the performance of PSCs, a new dynamic model is proposed to track the cells hysteresis by adding a variable voltage capacitor to the single, double, and triple diode models. The aim with adding the variable capacitor is to emulate the effect of charge accumulation at the interfaces of the device. Three modified models are presented based on one or more variable capacitors at single, double, and triple diode models. Where, the triple diode model with third order variable voltage capacitor model is the best among all models by 74.9% enhancement in comparison with single diode model. Also, the Equilibrium optimization algorithm (EOA) is proposed to determine the parameters of the proposed dynamic model based on the triple diode model. In comparison with the experimental data, for both forward and reverse scans, the findings showed that the suggested model accurately reflects cell performance. Added to that, the EOA finds well the optimal model parameters considering the root mean square as primary objective function. Based on the simulation results, it was proved that the proposed model gives very close results to reality.

In this paper the modelling of the photovoltaic module has been applied through the experimental flash test data of industrial photovoltaic module. The theoretical background of the single diode model and the double diode model of the photovoltaic module have been described and similar procedures for the extraction and estimation of the unknown parameters have been proposed. The Levenberg-Marquardt optimization algorithm has been used to minimize objective function as a set of five nonlinear equations that were written for five equilibrium points on the current-voltage curve of the photovoltaic module. The single diode model and double diode model have then been simulated with the estimated unknown parameters and a very good matching between flash test data and a simulation data.

This paper compares two methods for extraction of photovoltaic (PV) module parameters. The numerical method called Gauss-Seidel, used on the single diode model, and the analytical method applied on the double diode model. Both methods are based on the manufacturer’s datasheets to extract the five parameters. These methods are validated using monocrystalline PV module. The modified mathematical modeling equations are classified into models 1 to 5. Only model 2 and model 4 are used for the analytical method. Each model is based on the different combinations of the ideality factor, the series resistance, photocurrent and saturation current plotted under varying temperature and irradiance. The accuracy of PV module based on both methods is evaluated by Absolute Error (AE). In order to evaluate the effectiveness and accuracy of the proposed methods, the equations were implemented in the MATLAB/Simulink environment and the results obtained are compared with extracted experimental data.

This research paper systematically reviewed and investigated single diode model and double diode model of a solar photovoltaic systems in terms of accuracy, differences under major unknown PV parameters, different optimization and fabrication. This research paper reviewed the differences and the similarities between the single diode model and double diode model. From the review, it was clear that single diode model has less computation time and number of unknown parameters compared to double diode model. The double diode model on its own superiority is more accurate under solar shading condition effect than single diode model but single diode model performs better under high insolation levels. None of the two models is superior than the other but the solar photovoltaic modelers/installers should bear the solar irradiance of the environment before installation

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module. “Matlab/Simulink” is used to model and simulate the PV models because it is considered a major software for modeling, analyzing, and solving dynamic system real problems. In this work, a new modeling method based on the “Multiplexer and Functions blocks” in the "Matlab/Simulink Library" is presented. The mathematical analysis of single and double diodes is conducted on the basis of their equivalent circuits with simple modification. The corresponding equations are built in Matlab by using the proposed method. The unknown internal parameters of the PV panel circuit are extracted by using the PV array tool in Simulink, which is a simple method to obtain the PV parameters at certain weather conditions. Double-diode model results are compared with the single-diode model under various irradiances and temperatures to verify the performance and accuracy of the proposed method. The proposed method shows good agreement in terms of the I–V and P–V characteristics. A monocrystalline NST-120 W PV module is used to validate the proposed method. This module is connected to a variable load and tested for one summer day. The experimental voltage, current, and power are obtained under various irradiances and temperatures, and the I–V and P–V characteristics are obtained.

It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.

Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm

Precision parameter extraction for Multi-Junction Solar Cells (MJSCs) is paramount for reliable simulation and performance optimisation, but is critically hindered by the models’ severe non-linearity and multi-modality. This research suggests implementing the Bonobo Optimiser Algorithm (BOA) to overcome this challenge. The core novelty lies in BOA's first application of high-precision parameter estimation for MJSCs, based on the Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM). Additionally, statistical validation using ANOVA and the Friedman test confirms the effectiveness of the BOA for parameter estimation of the Thin triple-junction CTJ30 and multi-junction solar cell, as well as the MJSC, under various operating temperature conditions (41.5 °C, 51.3 °C, and 61.6 °C). Minimisation of RMSE is an objective function to optimise parameters. Statistical analysis of 30-run simulations confirms BOA’s superior consistency and minimal variance. Critical findings show that for the CTJ30 cell, BOA attained a consistent best RMSE of 2.513060E-03, 2.510171E-03 and 2.510387E-03 across SDM, DDM, and TDM, respectively. For MJSC datasets at 41.5 °C, BOA achieved the best RMSE values of 4.361990E-05 for the SDM, 2.768602E-05 for the DDM, and 2.827875E-05 for the TDM. At 51.3 °C, the best RMSE values were 5.434700E-05 for the SDM, 2.641600E-05 for the DDM, and 2.347326E-05 for the TDM. At 61.6 °C, the best RMSE values of 1.657126E-05, 2.693035E-05 and 1.556626E-05 for the SDM, DDM, and TDM, respectively, demonstrate exceptional precision. Empirical investigations and comparisons with the most recent state-of-the-art algorithms and conventional algorithms are used for validation and justification. Statistical ANOVA and the Friedman test validation and comparison investigations demonstrate the suggested BOA precision and consistency, which may replicate experimental datasets with minimal statistical lapses and reduced variance. A comprehensive statistical analysis of the CTJ30 and MJSCs PV datasets demonstrates that the proposed BOA is reliable in assisting quality control in high-efficiency photovoltaic systems.

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.

Parameter extraction of solar cell models using improved shuffled complex evolution algorithm

In this study, an application of Manta Ray Foraging Optimization (MRFO) algorithm is demonstrated for PV parameter extraction. The MRFO is utilized to extract the parameters of both single diode and double diode model R.T.C. France silicon solar cell. The algorithm is easy to implement with less computational time. The MRFO optimizer is compared to Artificial Bee Optimization (ABO) and Flower Pollination Algorithms (FPA) algorithms. The results demonstrated the ability of MRFO for parameter extraction.

In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

Photovoltaic parameter estimation using honey badger algorithm and African vulture optimization algorithm

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.