Parameter identification of solar cells and fuel cell using improved social spider algorithm

A binary social spider algorithm for uncapacitated facility location problem

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.

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.

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

The critical worldwide revolution towards clean energy has prompted the improvement of studies on the fabrication of high-performance solar cells. In this regard, rendering an accurate model of the solar cell for performance evaluation in the simulation could be essential. So far, several models have been proposed for the solar cell, including single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). By increasing the number of diodes considered in the equivalent circuit in order to deliver a more accurate model, the number of unknown parameters which must be identified will be increased as well. Therefore, presenting an efficient algorithm to estimate these parameters becomes an interesting issue in recent years. In this study, an improved optimization algorithm, called springy whale optimization algorithm (SWOA), is proposed to estimate the model parameters of solar cells. SWOA is a generalization of the WOA and has the advantages of high convergence speed, global search capability, and high robustness over it. In order to inquire the efficiency of SWOA, this algorithm is posed to estimate the parameters of models of solar cells and photovoltaic (PV) modules as well; the simulation results authenticate the supremacy of the proposed algorithm. Furthermore, the effectiveness of SWOA algorithm in the practical application has been evaluated using commercial modules, including polycrystalline (SW255), multi-crystalline (KC200GT), and monocrystalline (SM55). This assessment is carried out for various operating conditions under different irradiance and temperature conditions, which yield variations in the parameters of the PV model. The results obtained from various experimental setups confirm the high performance and robustness of the proposed algorithm.

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.

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.

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.

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

It is crucial for the photovoltaic system to have an accurate model and well‐estimated parameters to further increase conversion efficiency. Most existing methods for identifying photovoltaic model parameters have problems such as high computational cost, local optimum trouble, or difficulty in providing the best performance due to complex adjustments of algorithm parameters. To improve these defects, a hunter‐prey optimization algorithm coordinating mutual benefit and sharing and interactive learning activities (EHPO) is proposed. First, based on hunter‐prey optimization, timely information sharing with the mutual benefit and sharing mechanism was achieved when the algorithm was applied to search for prey, thus improving the searching precision and convergence rate of the algorithm. Second, hunters used the history optimization information and useful information from peers to guide the search direction, thus balancing the global and local development abilities of the algorithm. Finally, a lens imaging backward learning strategy was adopted to prevent the algorithm from falling into the local optimum, thus increasing the diversity of varieties and the probability of finding the global optimal solution. The simulation results of the single‐diode model (SDM), double‐diode model (DDM), triple‐diode model (TDM), and other PV models in different environmental conditions show that the improved EHPO algorithm is more advantageous for parameter extraction than other advanced metaheuristic algorithms. This study demonstrates that EHPO is an accurate and reliable tool for predicting the parameters of PV models.

Random learning gradient based optimization for efficient design of photovoltaic models

Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm

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.

Photovoltaic parameter estimation using honey badger algorithm and African vulture 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.