Solar Cell Model Parameters Research Articles

A novel artificial hummingbird algorithm improved by natural survivor method

The artificial hummingbird algorithm (AHA) has been applied in various fields of science and provided promising solutions. Although the algorithm has demonstrated merits in the optimization area, it suffers from local optimum stagnation and poor exploration of the search space. To overcome these drawbacks, this study redesigns the update mechanism of the original AHA algorithm with the natural survivor method (NSM) and proposes a novel metaheuristic called NSM-AHA. The strength of the developed algorithm is that it performs population management not only according to the fitness function value but also according to the NSM score value. The adopted strategy contributes to NSM-AHA exhibiting powerful local optimum avoidance and unique exploration ability. The optimization ability of the proposed NSM-AHA algorithm was compared with 21 state-of-the-art algorithms over CEC 2017 and CEC 2020 benchmark functions with dimensions of 30, 50, and 100, respectively. Based on the Friedman test results, it was observed that NSM-AHA ranked 1st out of 22 competitive algorithms, while the original AHA ranked 8th. This result highlights that the NSM update mechanism provides a remarkable evolution in the convergence performance of the original AHA algorithm. Furthermore, two constrained engineering problems including the optimization of single-diode solar cell model (SDSCM) parameters and the design of a power system stabilizer (PSS) are solved with the proposed algorithm. The NSM-AHA algorithm provided better results compared to other algorithms with a value of 9.86E − 04 root mean square error for SDSCM and 1.43E − 03 integral time square error for PSS. The experimental results showed that the proposed NSM-AHA is a competitive optimizer for solving global and engineering problems.

Parameter extraction of solar cell models using improved war strategy algorithm

A study proposed an improved war strategy algorithm called the chaos genetic manifestation war strategy algorithm (CGM-WSO) to extract parameters of solar cell model. The CGM-WSO algorithm incorporates three improvement measures. First, it introduces the logistic–tent chaos map to enable random perturbation during initialization. Second, it utilizes the crossover and mutation operators of the genetic algorithm to enhance the algorithm’s ability to escape local optima. Finally, the Lambert W function is employed to obtain explicit analytic expressions of single and double diode models. The performance of the CGM-WSO algorithm was validated using several cell’s models, and the results demonstrated significantly improved accuracy, reliability, and convergence speed compared to other algorithms.

Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters

<abstract> <p>This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial weights to achieve a balance between exploration and exploitation. The DMOWOA performances are tested through the solving of the single diode model, double diode model, and photovoltaic (PV) modules. Finally, the DMOWOA is compared with six well-known algorithms and other optimization methods. The experimental results demonstrate that the proposed DMOWOA exhibits remarkable competitiveness in convergence speed, robustness, and accuracy.</p> </abstract>

Deep ensemble inverse model for image-based estimation of solar cell parameters

Physical models can help improve solar cell efficiency during the design phase and for quality control after the fabrication process. We present a data-driven approach to inverse modeling that can predict the underlying parameters of a finite element method solar cell model based on an electroluminescence (EL) image of a solar cell with known cell geometry and laser scribed defects. For training the inverse model, 75 000 synthetic EL images were generated with randomized parameters of the physical cell model. We combine 17 deep convolutional neural networks based on a modified VGG19 architecture into a deep ensemble to add uncertainty estimates. Using the silicon solar cell model, we show that such a novel approach to data-driven statistical inverse modeling can help apply recent developments in deep learning to new engineering applications that require real-time parameterizations of physical models augmented by confidence intervals. The trained network was tested on four different physical solar cell samples, and the estimated parameters were used to create the corresponding model representations. Resimulations of the measurements yielded relative deviations of the calculated and the measured junction voltage values of 0.2% on average with a maximum of 10%, demonstrating the validity of the approach.

Comparesion the electrical parameters of photovoltaic cell using numerical methods

For a research problem: as a single-diode model (electrical circuit) is difficult to discover the precise answer to employing analytical approaches, develop and compute the electrical parameters of the PV cell roughly using numerical algorithms. Therefore, the goal of this work is to create an algorithm that aids in the approximate solution of the electrical parameters of solar cells. Three methods have been proposed for these calculations, each of which has a quicker calculation time and a higher level of accuracy.
 By streamlining the calculation process, the proposed method solves the problems of complexity and precision. The I-V and P-V characteristic curves of solar cells can then be utilized to compare the efficacy of the tested methods. In addition, the analysis of root mean square error indicates that the proposed method is more applicable than alternative methods. In fact, this extraction procedure can be regarded as an efficient and precise method for estimating the single diode model parameters of solar cells.
 The results indicate that this precise and effective strategy can play an important role in the retrieval of single diode model parameters. In fact, the method proposed in this paper makes numerically implementing parameter models in technology simpler. In addition, it provides an optimization suggestion for the production of solar cells

Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an Intelligent Optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function

In this paper, the explicit equation of the single diode model (SDM) expressed by the Lambert W function was reduced to its simplified form through variable replacement; then the simplified explicit equation was combined with an intelligent optimization algorithm to estimate the SDM parameters of solar cells and PV modules. To evaluate the parameter extraction performance of the new method, eight typical intelligent optimization algorithms were combined with the implicit, explicit, and simplified explicit equation to extract the SDM parameters of a solar cell and three types of PV modules. The results show that the new method not only improves the accuracy of parameter extraction but also enhances the robustness and convergence speed. Most importantly, the new method can nearly improve the parameter extraction accuracy of a poor-performing algorithm in traditional methods to the level of other well-performing algorithms without enhancing the algorithm itself. In a word, this study offers a new choice for a more accurate and reliable extraction of SDM parameters from both solar cells and PV modules.

Modeling, Simulation and Extraction of Model Parameters of Dye-Sensitized Solar Cells (DSSCs) using Different Single-Diode Models

Modeling, simulation and extraction of model parameters of four DSSCs was carried out using four different models. The results show that model parameters depend on of the types of models and dyes (or DSSCs). Also, the fewer the number of model parameters the lesser the computation efforts and knowledge about the internal features of the photovoltaic system and vice versa. In addition, the photocurrent Iph of a DSSC is constant for all models. Furthermore, the diode reverse saturation current Io is inversely proportional to the absolute values of the series resistance Rs and shunt resistance Rsh. Moreover, the modified diode ideality factor depends on the number of model parameters and nature of dye/DSSC. However, models with higher number of model parameters (4 and 5) involving resistances manifest parameter irregularity. Nevertheless, the negative sign or complexity of the model parameters does not render them undesirable elements for researchers to use them in their application. Thus, the overall result shows that all the models used produced good I-V curve fit for all the solar cells studied irrespective of the presence of parameter irregularity.Keywords: Modeling, Simulation, Model parameter, DSSCs, Single diode model, Parameter irregularity.

Solar Photovoltaic Cell Parameter Identification Based on Improved Honey Badger Algorithm

Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem.

Approximation of photovoltaic characteristics curves using Bézier Curve

Accuracy of solar cell model parameters is now a requirement for the manufacturing of appropriate photovoltaic models. Consequently, the methodologies of the parameters had already attracted immense interest among researchers over the years. This paper deals about the approximation of the characteristics curves of a solar cell using the Bézier curve method. This technique is selected to overcome the problems of the non-linearity, complexity, multivariate and multimodal of the characteristic's curves. The Bézier curves method is a parametric curve used throughout computer graphics and related disciplines. It is a robust methodology for constructing free-form curves, surfaces, shape design, and geometric representation of various products. Six different cases are carried out to test the validity of the proposed method, they are: (i) organic flexible dual junction amorphous silicon solar cell, (ii) silicon solar cell, (iii) Schutten Solar STP6-120/36 polycrystalline photovoltaic module, (iv) Schutten Solar STM6-40/36 monocrystalline photovoltaic module, (v) Photowatt-PWP 201 polycrystalline photovoltaic module and (vi) 230-W multi-silicon photovoltaic module under partial shading conditions. The technique is implemented and adapted by the mean of the De Casteljo algorithm to optimize the control points and minimize the distance between the experimental and calculated points. It is based on subdividing the curve into various segments and controlled by control points using the De Casteljo algorithm to minimize the distance between the experimental and theoretical points. The performance of the approximated characteristics curves is compared with experimental data as well as recent algorithms and techniques from the literature. For more validity, statistical error metrics from literature are computed and compared to ensure the accuracy of the approximated results and the performance of the proposed method. For instance, the comparison is carried out by resorting to the Individual Absolute Error (IAE) and the Relative Error (RE). Further, the Bias metrics and accuracy are calculated using the Autocorrelation Function (ACF), Tracking Signal (TS), and Normalized Forecast Metric (NFM). The proposed method provides a low error for the six case studies and better than that obtained by the other algorithms.

Using a novel optimization algorithm for parameter extraction of photovoltaic cells and modules

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.

Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions.

Turbulent Flow of Water-Based Optimization Using New Objective Function for Parameter Extraction of Six Photovoltaic Models

With the development of new energy power systems, the estimation of the parameters of photovoltaic (PV) models has become increasingly important. Weather changes are random; therefore, the changes in the PV output power are periodic and nonlinear. Traditional power prediction methods are based on linearity, and relying only on a time series is not feasible. Consequently, metaheuristic algorithms have received considerable attention to extract the parameters of solar cell models and achieve excellent performance. In this study, the Turbulent Flow of Water-based Optimization (TFWO) is used to estimate the parameters of three traditional solar cell models, namely, Single-Diode Solar Cell Model (SDSCM), Double-Diode Solar Cell Model (DDSCM), and Three-Diode Solar Cell Model (TDSCM), in addition to three modified solar cell models, namely, modified SDSCM (MSDSCM), modified DDSCM (MDDSCM), and modified TDSCM (MTDSCM). Moreover, a polynomial equation of five degrees for the sum of squared errors (PE5DSSE) between the measured and calculated currents was used as a new objective function for extracting the parameters of the solar cell models. The proposed objective function delivered improved prediction accuracy than common objective functions. Experimental results revealed the effectiveness of TFWO compared with six counterparts, namely, “Tunicate Swarm Algorithm (TSA), Grey wolf optimizer (GWO), modified particle swarm optimization (MPSO), Cuckoo Search algorithm (CSA), Moth flame optimizer (MFO) and Teaching Learning based optimization algorithm (TLBO),) for all the traditional and modified solar cell models based on the optimal parameters extracted using best PE5DSSE values.

In-Situ Measurement of Power Loss for Crystalline Silicon Modules Undergoing Thermal Cycling and Mechanical Loading Stress Testing

An in-situ method is proposed for monitoring and estimating the power degradation of mc-Si photovoltaic (PV) modules undergoing thermo-mechanical degradation tests that primarily manifest through cell cracking, such as mechanical load tests, thermal cycling and humidity freeze tests. The method is based on in-situ measurement of the module’s dark current-voltage (I-V) characteristic curve during the stress test, as well as initial and final module flash testing on a Sun simulator. The method uses superposition of the dark I-V curve with final flash test module short-circuit current to account for shunt and junction recombination losses, as well as series resistance estimation from the in-situ measured dark I-Vs and final flash test measurements. The method is developed based on mc-Si standard modules undergoing several stages of thermo-mechanical stress testing and degradation, for which we investigate the impact of the degradation on the modules light I-V curve parameters, and equivalent solar cell model parameters. Experimental validation of the method on the modules tested shows good agreement between the in-situ estimated power degradation and the flash test measured power loss of the modules, of up to 4.31 % error (RMSE), as the modules experience primarily junction defect recombination and increased series resistance losses. However, the application of the method will be limited for modules experiencing extensive photo-current degradation or delamination, which are not well reflected in the dark I-V characteristic of the PV module.

Modelling and Parameters Extraction of Flexible Amorphous Silicon Solar Cell a-Si:H

The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic parameter extraction method of flexible hydrogenated amorphous silicon a-Si:H solar cell, based on the meta-heuristic firefly algorithm (FA). The characteristics of solar cells are non-linear, multivariable and multi-modal and difficult to identifies the electrical intrinsic parameters by conventional and analytical methods with high accuracy. Recently, the firefly algorithm has attracted the attention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current–voltage curve. It is the absolute errors between the experimental and calculated current and voltage values. Furthermore, the obtained results of the proposed algorithm are compared with the results obtained by quasi-Newton method (Q-N) and self-organizing migrating algorithm (SOMA). Indeed, to validate the performance of the algorithm, the statistical analyses are carried out to measure the accuracy of the estimated parameters. In the end, the theoretical results of the firefly algorithm show an excellent agreement with experimental data and more accurate compared to other compared techniques.

A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models

Quickly, accurately and reliably extract the parameters of solar photovoltaic (PV) model is very critical to simulate, evaluate and control the PV systems. During the past few years, many analytical, numerical and meta-heuristic algorithms have been suggested to extract the parameters of PV models based on the experimental data. However, extracting the parameters of PV models is still a great challenge. In this paper, a new hybrid algorithm based on grey wolf optimizer and cuckoo search (GWOCS) is developed to extract the parameters of different PV cell models with the experimental data under different operating conditions. In GWOCS, a new opposition learning strategy for the decision layer individuals (i.e., α, β, and δ) is proposed to enhance diversity of GWO. The main advantage of GWOCS is its ability to balance between exploration and exploitation. The performance of GWOCS is firstly tested on 10 complex benchmark functions. Then, the GWOCS is applied to extract the parameters of several solar PV cell models under different operating conditions. The comprehensively experimental results show the GWOCS is a promising candidate approach to extract the parameters of solar PV models.

Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm

Solar cell is one of the important renewable energy resources, and it is considered a promising source for energy challenges in the future. The identification of solar cell model parameters is very important due to the control and the simulation of PV systems. In this paper, an enhanced teaching–learning-based optimization (ETLBO) algorithm is proposed and applied to estimate the photovoltaic cells parameter. The ETLBO is proposed to improve the performance of conventional TLBO and reduce its search space by adjusting the parameters which control the explorative and exploitative phases to achieve the suitable balancing. The proposed algorithm is validated using real dataset of photovoltaic single-diode and double-diode models. In addition, the proposed algorithm is tested on the dataset of two real PV panels (polycrystalline and monocrystalline). The results obtained by the proposed algorithm are compared with those obtained by other well-known optimization algorithms. All results prove the effectiveness and superiority of proposed algorithm compared with other optimization techniques.

Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models

Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.

An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models

Solar cells are one of the renewable energy sources that have been widely used. The parameters extraction plays an important role in the speed and accuracy of models designed for photovoltaic (PV) solar cells and modules. In recent years, the evolutionary algorithm (EA), swarm intelligence (SI), and other nature-inspired (NI) algorithms have been widely used for the parameters extraction of PV modules. This paper presents a new method by improving the existing Rcr-IJADE with an onlooker-ranking-based mutation scheme. This mutation scheme is an effective and efficient vectors selection mechanism for encountering the objective function containing a flat basin. The improved algorithm referred to as ORcr-IJADE, it is quickly and accurately extracted the parameters of solar cell models. 18 solar cell models and PV modules from several manufacturers were used to validate the algorithm. Comparative studies among the different algorithms were conducted using current-voltage (I-V) data. The results of ORcr-IJADE were compared with 31 state-of-the-art EA, SI, and NI algorithms. The results confirm the superiority of the proposed method, as the accuracy, the success rate and the convergence speed are better than the competitors. The proposed algorithm is useful in developing highly accurate solar PV models with less computational effort used.

Effects of Mechanical Damage and Temperature on the Electrical Performance of CIGS Thin-Film Solar Cells

In this paper, we investigate the effect of mechanical damage and temperature on copper indium gallium diselenide (CIGS) thin-film solar cells through experiments and modeling. After generating mechanical damage on CIGS solar cell with 20% increments, the electrical performance was measured (current–voltage curve) while temperature was varying from 10 to 70 °C at 20° increments. Other measured values are open-circuit voltage ( V oc), fill factor (ff), maximum power ( P max). Those electrical values ( V oc, ff, P max) are found as a function of temperature and percent damage. Moreover, the parameters of the single diode solar cell model were obtained as a function of temperature and percentage damage: light generation current ( IL ), saturation current ( I0 ), shunt resistance ( R sh), and series resistance ( R s ). Our paper contributes to the deeper understanding of the concurrent effect of temperature and mechanical damage to solar cells.

Parameters identification of photovoltaic solar cells and module using the genetic algorithm with convex combination crossover

ABSTRACTTo design a high-performance photovoltaic (PV) system, the parameters extraction of solar cell models is exceedingly crucial. A new variant of the genetic algorithm (GA) called Genetic Algorithm with Convex Combination Crossover (GACCC) is proposed to identify the unknown electrical parameters of different solar cell models, i.e. single diode, double diode, and PV module. GACCC is achieved by integrating a new crossover operation to maintain a good balance between the intensification of the best solutions and the diversification of the search space. To test the proposed GACCC, we have compared it to the basic GA and with other literature techniques. The results indicate a high performance of developed approach GACCC and a high accuracy of estimated parameters. In addition, the efficiency of the results is confirmed by the good agreement between the experimental I-V data and the simulated results in all cases.