Search

High-performance solar photovoltaic models rely on a precise understanding of solar photovoltaic (PV) cell parameters. This necessity arises from the importance of comprehending and optimizing the performance of photovoltaic systems to ensure reliable and efficient energy production. Nonetheless, due to the intrinsic nonlinear nature of solar photovoltaic systems, employing a reliable algorithm is essential for accurate modeling. In this article, an enhanced algorithm inspired by the behavior of the Tasmanian Devil, named Improved Tasmanian Devil Optimization (ITDO), is proposed to enhance the performance of the original TDO. Our algorithm includes improvements to the exploitation phase, increasing the frequency of prey detection and attacks in the target zone. The proposed method retains the original steps, with the exploration stage unchanged. However, the second step has been enhanced with an adaptation mechanism, and the final step has been improved to efficiently select the global optimum. These modifications do not impact the method’s complexity. To assess ITDO’s effectiveness, experiments were conducted using single, double, and PV module models. Thorough comparison with seven other algorithms revealed ITDO’s superior solution accuracy. Additionally, statistical analyses using Wilcoxon rank-sum and Friedman tests confirmed ITDO’s superiority as the most robust and efficient algorithm for parameter estimation in PV systems. [ABSTRACT FROM AUTHOR]

Developing an accurate mathematical model for parameter extraction in photovoltaic modules is a crucial endeavor in optimizing photovoltaic energy systems. This study seeks to assess and compare various analytical and numerical methods for extracting the primary five parameters of photovoltaic modules. Specifically, six established approaches based on a single diode model (SDM) are employed, including the methods introduced by Khan et al., Blas et al., Phang et al., Vika, Cubas et al., and Almonacid et al. The performance of these approaches is evaluated and compared under standard test conditions (STC) with a focus on maximum power point current and voltage. The analytical and numerical methods demonstrate their precision in predicting photocurrent-voltage (I-V) and power-voltage (U-V) curves, with the exception of the Almonacid et al. method, which tends to underestimate the I-V curve at the module's maximum power. Among these methods, the Phang et al. approach stands out, displaying a strong agreement between experimental data and the predicted curve. This is evidenced by the lower values of root mean square error (RMSE), mean bias error (MBE), normalized RMSE (NRMSE), mean absolute percentage error (MAPE), and absolute error (AE). These findings underscore the high quality of results obtained through the Phang et al. method. [ABSTRACT FROM AUTHOR]

Abstract Estimating parameters in solar cell models is crucial for simulating and designing photovoltaic systems. The single-diode, double-diode, and three-diode models represent these systems. Parameter estimation can be viewed as an optimization problem to minimize the difference between measured and estimated data. This study presents PV parameter estimation using the enhanced Sinh Cosh Optimizer (I_SCHO), incorporating trigonometric operators from the Sine Cosine Algorithm (SCA). This integration improves the algorithm’s ability to navigate complex search spaces, avoid local optima, and expedite convergence. Assessment criteria include runtime, convergence behaviour, minimum RMSE, and system reliability measured by SD. Results show that I_SCHO consistently delivers superior accuracy and reliability compared to other methods. Experiments were conducted on five solar cells: RTC France, Photowatt-PWP201, Kyocera KC200GT, Ultra 85-P, and STM6-40/36 module. The study also includes a comparative analysis using state-of-the-art algorithms, demonstrating I_SCHO’s efficiency through RMSE, Power Voltage (P-V) and Current Voltage (I-V) curves.

To achieve accurate PV cell parameter identification, a method combining the second‐order Bézier function with the adaptive war strategy optimization (AWSO) algorithm is proposed, the Bézier curve for fitting the PV output characteristic curve is given by using the linear connection between the filling factor and the control points of the second‐order Bézier function, and then, the Bézier‐AWSO model is constructed, and an adaptive weight‐based updating and allocation method is given to improve the global search capability of the AWSO algorithm in the problem of recognizing five important parameters of photovoltaic, and avoid falling into the local optimum in the process of recognition. Finally, the optimal solution for the unknown parameters in the single diode topology of silicon‐based photovoltaic cells is obtained by using the data points of the fitted curves and the AWSO algorithm, and the accuracy of the AWSO algorithm for photovoltaic cell parameter identification is verified through the analysis of the arithmetic examples and real experiments. The results demonstrate that the parameter identification of the proposed improved model reduces the average relative error by 0.5%–1% compared with that before the improvement under standard and non‐standard test conditions, which improves the accuracy of the 5‐parameter identification results of the silicon‐based PV cell and provides a reference for the subsequent fault analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction. [ABSTRACT FROM AUTHOR]

The performance of photovoltaic models depends significantly on the accuracy of their parameters, which are determined by the chosen method and objective function. Extracting these parameters accurately under different environmental conditions is essential to enhance reliability, accuracy, and minimize system costs. In this research, a novel technique is proposed for extracting the electrical parameters of the solar cell single diode model, including saturation current, serial resistance, parallel resistance, and ideality factor. To overcome the challenges posed by the chaotic behavior of the I-V curve equation, an improved Iterative Root-Finding algorithm is introduced. This algorithm acts as an optimization tool, increasing the likelihood of obtaining highly accurate solutions by minimizing the quadratic error between experimental and theoretical characteristics in a shorter time frame. The numerical and experimental results demonstrate the effectiveness of this approach in solar module modeling, showing squared errors approaching zero. This study opens new possibilities for improving the accuracy and reliability of photovoltaic models, leading to more efficient solar energy systems. [ABSTRACT FROM AUTHOR]

Developing an accurate mathematical model for parameter extraction in photovoltaic modules is a crucial endeavor in optimizing photovoltaic energy systems. This study seeks to assess and compare various analytical and numerical methods for extracting the primary five parameters of photovoltaic modules. Specifically, six established approaches based on a single diode model (SDM) are employed, including the methods introduced by Khan et al., Blas et al., Phang et al., Vika, Cubas et al., and Almonacid et al. The performance of these approaches is evaluated and compared under standard test conditions (STC) with a focus on maximum power point current and voltage. The analytical and numerical methods demonstrate their precision in predicting photocurrent-voltage (I-V) and power-voltage (U-V) curves, with the exception of the Almonacid et al. method, which tends to underestimate the I-V curve at the module’s maximum power. Among these methods, the Phang et al. approach stands out, displaying a strong agreement between experimental data and the predicted curve. This is evidenced by the lower values of root mean square error (RMSE), mean bias error (MBE), normalized RMSE (NRMSE), mean absolute percentage error (MAPE), and absolute error (AE). These findings underscore the high quality of results obtained through the Phang et al. method.

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels. [ABSTRACT FROM AUTHOR]

In order to optimize the performance of a Solar Photovoltaic (PV) system, it is necessary to develop an appropriate PV cell model and accurately determine the unknown parameters associated with the model. The process of extracting parameters for PV models is a complex optimization issue that involves nonlinearity and multiple models. Accurate estimation of the characteristics of PV units is crucial since these factors significantly affect the performance of PV systems in terms of power and current generation. Consequently, this research presents an advanced methodology, known as the Pelican Optimization Algorithm (POA), aimed to find the optimal values for the unspecified parameters of PV units. In this study, the Single Diode Model (SDM) is employed to analyze four datasets like RTC France, Photowatt-PWP201, STP-120/36, as well as STM6-40/36 PV panels. The POA algorithm is utilized to determine the unknown parameters of solar PV modules. Furthermore, to enhance the precision of the obtained solutions, this study incorporates the Newton–Raphson (NR) method into the POA algorithm. The POA achieves the optimum Root Mean Square Error (RMSE) values for the four PV models (RTC France, Photowatt-PWP201, STM6-40/36 and STP6-120/36) and the values are found to be 7.7298E-04, 2.0528E-03, 1.7220E-03 and 1.4458E-02 respectively. From the results, it is observed that, POA exhibit superior performance compared to the other MH optimization algorithms. Furthermore, the statistical findings show that the POA algorithm has a higher average robustness and accuracy than the other algorithms.

Everywhere in the globe, the total capacity of photovoltaic (PV) panels is expanding at an exponential rate. Arc faults, open-circuit (OC) faults, bypass diode failures, mismatch faults, and short circuit faults are only a few of the most common types of problems that may occur in PV arrays. Not recognizing and correcting these issues quickly might affect power plant production. Fault detection in PV modules helps stabilize PV plant output. Machine learning techniques can automatically identify PV module issues. This paper portrayed fault detection using Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Two methods identify PV defects based on normalizing factors. MLP has nonlinear problems and is slow to compute. The suggested RNN proved to be a superior detection approach for 10 weeks of testing on 2.4 KW monocrystalline solar panels. MLP has 75.62% fault detection accuracy whereas RNN has 98.95% in 4s-2p PV panels. Therefore, the findings of the simulation indicate that the proposed RNN technique achieves the necessary level of speed and accuracy. [ABSTRACT FROM AUTHOR]

Ongoing importance remains placed on parameter estimation in photovoltaic (PV) system design and simulation. Among the diode-based models utilized frequently are those consisting of a single diode, a double diode, and three diode. Minimizing the discrepancy between the calculated and measured values is often the primary aim when estimating parameters for these models. In recent years, the estimation of parameters in conventional PV models has been approached using various numerical, analytical, and hybrid techniques. However, these methods could be made more complex to produce credible results promptly and precisely. This article discusses the three fundamental PV models. A contemporary optimization algorithm, the Crayfish Optimisation algorithm (COA), is employed to extract the parameters for PV models. This includes the single-diode, double-diode, and three-diode models. An assessment uses to contrast the PV models. COA outperforms the subsequent competing algorithms, as demonstrated by the experimental results: Hunger Games Search, SOA, STOA, Synergistic Mimic Algorithm (SMA), TURBULAS Swarm Algorithm (TSA), and LAPOP (Lightning Attachment Procedure Optimisation) are all examples of optimization algorithms, along with HHO, HBO, LIPO, SOA, and STOA, respectively. By the negligible difference between measured and calculated data, this comparison illustrates that the parameters extracted by COA are optimal. As determined by the proposed COA algorithm, 0.00085477, 0.0010313, and 0.00092288 are the optimal RMSE values for SDM, DDM, and TDM.

Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.

This research addresses the pressing need for clean energy solutions by focusing on the increasing adoption of photovoltaic (PV) modules as alternatives to fossil fuel-based energy sources. Despite their promise, PV modules face challenges in maintaining efficiency under varying environmental conditions. To tackle this, the study develops a one-diode model of PV modules using the Gravitational Search Algorithm (GSA) to obtain the optimal PV parameters so that the performance of the PV modules across diverse weather conditions can be obtained. The integration of a microcontroller and sensors in the experimental setup allows for real-time monitoring of critical environmental variables such as irradiance, temperature, and humidity, alongside voltage and current measurements. While there are certain constraints, such as the sensitivity of sensor data to weather conditions and the significant dependency of estimated parameters on measurements. However, by analyzing the performance of three specific PV modules SOLTECH215, PHOTOWATT220, and KC200GT under varying conditions, this research provides invaluable insights into optimizing energy production and efficiency in practical applications. By bridging theoretical modeling with experimental validation, it lays the groundwork for more efficient and reliable solar energy systems, thus driving the transition towards a cleaner and more sustainable energy future.

In order to optimize the performance of a Solar Photovoltaic (PV) system, it is necessary to develop an appropriate PV cell model and accurately determine the unknown parameters associated with the model. The process of extracting parameters for PV models is a complex optimization issue that involves nonlinearity and multiple models. Accurate estimation of the characteristics of PV units is crucial since these factors significantly affect the performance of PV systems in terms of power and current generation. Consequently, this research presents an advanced methodology, known as the Pelican Optimization Algorithm (POA), aimed to find the optimal values for the unspecified parameters of PV units. In this study, the Single Diode Model (SDM) is employed to analyze four datasets like RTC France, Photowatt-PWP201, STP-120/36, as well as STM6-40/36 PV panels. The POA algorithm is utilized to determine the unknown parameters of solar PV modules. Furthermore, to enhance the precision of the obtained solutions, this study incorporates the Newton–Raphson (NR) method into the POA algorithm. The POA achieves the optimum Root Mean Square Error (RMSE) values for the four PV models (RTC France, Photowatt-PWP201, STM6-40/36 and STP6-120/36) and the values are found to be 7.7298E-04, 2.0528E-03, 1.7220E-03 and 1.4458E-02 respectively. From the results, it is observed that, POA exhibit superior performance compared to the other MH optimization algorithms. Furthermore, the statistical findings show that the POA algorithm has a higher average robustness and accuracy than the other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a numerical method for estimating four physical parameters of a single-diode circuit model based on manufacturer’s datasheet. A system of four non-linear equations are formed based on three key points of PV characteristics. The photocurrent, saturation current, ideality factor and the series resistance are solved iteratively using the proposed method. The suggested method is validated using RTC France solar cell, Chloride CHL285P and Photowatt PWP210 modules and the results are verified with respect to the in-field outdoor measurements. The proposed method shows a good agreement with the experimental data. Lastly, The model chosen is simulated under MATLB environment to assess the effects of external physical weather conditions, that is, temperature and solar irradiance. The advantage of the proposed method with respect of existing numerical techniques is that it converged faster than the widely used Newton method. Modeling of PV cell/module is essential in predicting performance of photovoltaic generators at any operating condition.

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Studies are being actively conducted to improve the efficiency and performances of photovoltaic and thermo-photovoltaic systems and cells. To ameliorate designs and performances of these systems, where semiconducting junctions are generally used, it is very necessary to understand the electrical properties of these devices and conduction processes occurring across the interface of the structure. It is well known that operation and performances of photovoltaic components are strongly related to what is called dark current. Knowing the origin of this current allows improving the structure's configuration of a device, for example by adjusting the semiconducting layers thicknesses and their doping concentrations. However, solar cell models have a non-linear form with numerous parameters. To obtain accurate parameter values, assumptions that differ from real operating conditions must be made to avoid computational complexity. In this work, we proposed a new analytical approach to analyze the experimental current density-voltage of the solar cell models, and to the numerically extraction of the intrinsic solar cells parameters (i.e., the ideality coefficient and the series resistance). Our approach gives very good results. Moreover it is very simple to use and presents the advantage of being independent of the voltage step in contrary to the derivative and to the integral. We have then applied our technique to a whole solar cell current density-voltage curve and the results are very good. [ABSTRACT FROM AUTHOR]

In this paper, a new analytical approach based on an explicit model with one shape parameter has been proposed to forecast the peak power of the photovoltaic modules (PV) under varying operating conditions. A new analytical model of shape parameter has been developed according the relationship between the parameters of the explicit model and those of the single diode model (SDM). Furthermore, a novel open circuit voltage model as a function of solar irradiation and PV module temperature has been proposed. Temporal variations of PV module temperature and solar irradiation during one reference day have been used to extract the explicit model parameters and then to forecast the peak power for all other days. The proposed method is validated using measured data of different PV module technologies operating outdoor recorded by National Renewable Energy Laboratory (NREL). The results have shown a good agreement between the experimental and the optimized data of the I-V characteristics and the maximum power point for a reference day and the normalized error has not exceed 2.9%. Moreover, the predicted values of maximum power point in two other arbitrary days have a good agreement with experimental ones, and the normalized error has not exceeded 3.6%.

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. [ABSTRACT FROM AUTHOR]

In this work, a modified stochastic fractal search algorithm is proposed for effectively estimate the unknown model parameters of the single diode model and the double diode model of solar cells and photovoltaic modules. This algorithm modifies the diffusion process and the update processes of the original stochastic fractal search algorithm and employs a population size reduction mechanism and thus simplifies the implementation of the original algorithm and achieves better performance for the parameter estimation of solar cells and PV modules. Firstly, the algorithm is tested on three widely used estimation benchmark cases and compared with other seven state-of-the-art algorithms. The proposed algorithm shows its advantages in the aspects of accuracy, convergence speed, and stability over the other studied algorithms. In 100 independent runs with the maximum number of fitness function evaluations equal to 50000, the proposed algorithm achieved the best known solutions with 100% probability and the standard deviation of the 100 RMSE values is of the order of 10−17. Then, the proposed algorithm is used to estimate the unknown single diode model parameters and double diode model parameters of three different types of PV modules, i.e. Multi-crystalline S75, Mono-crystalline SM55, and Thin-film ST40 under different irradiance and temperature conditions, and the algorithm also achieves sufficiently accurate models. The root mean square of the error (RMSE) values between the obtained models and the actual current data are of the order of 10−2 or 10−3. In view of its effectiveness and practicability, the proposed algorithm can be used as a new tool for the parameter estimation of solar cells and PV modules.

Solar energy is a good option to replace the fossil driven energy market. Research is still ongoing to make solar technologies more efficient and affordable. Modeling is key in this area. Parameter extraction of PV modules enables easier simulation and accurate modeling of various PV cells/modules. The parameters are namely: (I) the photo generated current, Iph, (II) the reverse saturation current, Is, (III) ideality factor, n, (IV) the series resistance, Rs and (V) the shunt resistance, Rsh. This research work extracted the equivalent circuit parameters of polycrystalline and amorphous solar panels. An 11 V polycrystalline and 6 V amorphous solar panels were illuminated with a 500 W halogen lamp to generate IV characteristics with the aid of a data capture device. A Matlab/Simulink model was modified using the single diode equation to model the two test solar panels. The Orthogonal Distance Regression (ODR) method from the origin lab was adopted to solve the nonlinear/transcendental equation of the solar cell/module with the single diode model to determine the parameters of a polycrystalline and amorphous solar panel. The results show a variation in the parameters of the two test solar panels. The polycrystalline indicates a higher Rsh and Iph with low Rs and n values, which result to high efficiency. The amorphous panel shows higher n and Rs values, which makes it have low efficiency. The polycrystalline solar panel has smaller residual sum of square (RSS) which makes it a better retrieval while the amorphous solar panel has a higher residual sum of square (RSS), the ODR method for polycrystalline was more accurate than the amorphous solar panel as observed from the validation results of the two test panels. [ABSTRACT FROM AUTHOR]

The evaluation of photovoltaic device performance is based on the I-V characteristic curve. Unfortunately, the data sheets provided by the manufacturers only include data at standard test conditions, which requires the construction of a model capable of simulating the electrical behavior of these devices at different conditions. The identification of the model from the I-V curve is a challenging task due to the strong nonlinear relationship between the model parameters. This paper proposes a hybrid analytical/iterative method to extract the parameters of the single diode model. In this method, the four parametersa, Rsh, Io and Ipv are calculated directly by their explicit expressions. The properties of the Lambert function are still used to untie the coupling between the parameters. The parameter Rs is determined using an iterative process based on the minimization of the error between the theoretical and experimental data. Accuracy criteria have been introduced to demonstrate the reliability of the proposed method and its accuracy compared to other methods in the literature. The robustness of the proposed mathematical model in the face of variations in the initial conditions chosen has also been demonstrated.

• A high-accuracy and low-cost method for PV model parameter extraction is proposed. • The best model accuracy has been obtained in two most commonly-used case studies. • The method is applicable to incomplete or noisy I-V data. • The robustness of the method is verified by more than one million I-V curves. In this work, a state-of-the-art deterministic method is proposed for photovoltaic single diode model parameter extraction from experimental current–voltage (I-V) curves. This new method takes advantage of the numerical derivative (d I /d V) information, such that a high-quality solution can be obtained by linear least squares technique. The solution can be further improved through a two-step nonlinear optimization procedure using the trust-region-reflective or Levenberg-Marquardt algorithm. Compared with previous methods, the proposed method has achieved the best accuracy with the lowest computation cost up till now in the benchmark test of two commonly-used case studies. When comparing the computation costs of different works, it is pointed out for the first time that not only the number of iteration steps but also the single-step computation complexity should be taken into account if different deterministic optimization algorithms are implemented. Furthermore, the robustness of the proposed method is verified through a large-scale dataset containing more than one million I-V curves, showing that the method can be used for realtime monitoring of photovoltaic modules. Finally, a MATLAB app is developed and freely provided to the public. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is proposing a new approach for retrieving the five parameters of the single diode equivalent model (SDM) of photovoltaic cells/modules including the series and shunt resistances. The least square method is used as an error minimization technique for fitting the non-linear transcendental model equation of the solar panel to the measured I–V characteristics. Newton Raphson method is applied to solve the system of five non-linear equations which represent the error of each parameter. Initial guess values are calculated with an optimised algorithm depending on information extracted from the same measured data. MATLAB programming script was used in all implementation steps. This approach is useful for a wide variety of applications where the five SDM parameters have to be determined from the measured curves, particularly, for self-fabricated cells/modules or in case of no available datasheet. One of the strengths of this method is the higher level of accuracy because of the absence of mathematical simplifications and physical assumptions. The method was validated on different types of PV devices, including a crystalline silicon cell, a polycrystalline module, and an amorphous module by using measurement data obtained at a wide range of solar irradiance conditions and temperatures.

In this paper, an improved hybrid algorithm called differential evolution with integrated mutation per iteration (DEIM) is proposed to extract five parameters of single-diode PV module model obtained by combining differential evolution (DE) algorithm and electromagnetic-like (EML) algorithm. The EML algorithm's attraction-repulsion idea is employed in DEIM in order to enhance the mutation process of DE. The proposed algorithm is validated with other methods using experimental I-V data. The results of presented method reveal that simulated I-V characteristics have a high degree of agreement with experimental ones. The proposed model has an average root mean square error of 0.062A, an absolute error of 0.0452A, a mean bias error of 0.006A, a coefficient of determination of 0.992, a standard test deviation around 0.04540, and 15.33sec as execution time. The results demonstrate that the proposed method is better in terms of the accuracy and execution time (convergence) when compared with other methods where provide less errors.

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling. [ABSTRACT FROM AUTHOR]

A mathematical model with precise parameters is required to analyze the performance of a solar photovoltaic generating system. This technical note presents a unique scheme for accurately estimating the parameters of a solar PV system. The proposed method is a combination of quantum-based avian navigation optimizer (QANO) and Newton–Raphson (NR) method. QANO algorithm, a novel metaheuristic algorithm, is employed for identifying a global optimum solution with optimal parameters which suit well the given experimental solar cell/module. The NR method, on the other hand, is used to solve nonlinear equations during the objective function calculation process. Most of the algorithms estimate the parameters based on the conventional objective function, which do not consider the nonlinearities of the I–V characteristics. Such inaccurate models may not be reliable for real-time applications. In this work, an objective function is formulated which offers a more accurate parameters of the equivalent PV models without neglecting nonlinearities. The proposed method is applied to estimate parameters for a single diode model (SDM), a double diode model (DDM), and a PV module. The efficacy of the proposed QANO algorithm is compared to the results of other state-of-the-art algorithms reported in the literature. The proposed algorithm achieves an RMSE of 7.7300630E−04 for SDM and 7.5248E−04 for DDM, which are lower than most of the existing algorithms. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) parameters estimation from the experimental current and voltage data of PV modules is vital for monitoring and evaluating the performance of PV power generation systems. Moreover, the PV parameters can be used to predict current-voltage (I-V) behavior to control the power output of the PV modules. This paper aimed to propose an improved differential evolution (DE) integrated with a dynamic population sizing strategy to estimate the PV module model parameters accurately. This study used two popular PV module technologies, i.e., poly-crystalline and mono-crystalline. The optimized PV parameters were validated with the measured data and compared with other recent meta-heuristic algorithms. The proposed population dynamic differential evolution (PDDE) algorithm demonstrated high accuracy in estimating PV parameters and provided perfect approximations of the measured I-V and power-voltage (P-V) data from real PV modules. The PDDE obtained the best and the mean RMSE value of 2.4251E-03 on the poly-crystalline Photowatt-PWP201, while the best and the mean RMSE value on the mono-crystalline STM6-40/36 was 1.7298E-03. The PDDE algorithm showed outstanding accuracy performance and was competitive with the conventional DE and the existing algorithms in the literature. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are subjected to several environmental disturbances, one such a disturbance is partial shading, which adversely alters the characteristics of the photovoltaic system. Therefore, under Partial Shading Condition (PSC) there is a need to develop a complete analytical model of the PV system to investigate the most appropriate maximum power point tracking (MPPT) strategy in larger PV Systems. Based on the necessity, a precise analytical model of the PV system under PSC considering the effect of both series and shunt resistance is developed. The effects of temperature changes, irradiation changes, and effect for change in series, and shunt resistance and PSCs on electrical characteristics of the PV array are examined. A comparison was made with real-time data over the proposed and existing models during various PSCs. The results proved that the reliability of the proposed model is good, and it can be used to model different ranges of photovoltaic systems for both standalone and grid-integrated systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach based on Lambert W-function to extract the electrical parameters of photovoltaic (PV) panels. This approach can extract the optimal electrical characteristics of the PV panel under variable conditions of irradiation and temperature. Three benchmarking panels (shell SP70 monocrystalline silicon, shell ST40 thin film, and KC200GT Polycrystalline Silicon) are demonstrated and analyzed considering the electrical characteristics provided by the manufacturers. A comprehensive assessment is carried out under different weather condition to validate the capability and the robustness of the proposed approach. Furthermore, the simulated output characteristics of the three modules Photovoltaic are almost comparable and reproduce faithfully the manufacturer’s experimental data The novelty of this study is the using a new hybrid analytical and numerical method that straight forward and effective given value of Root mean square error less than those obtained by others methods that indicate the estimated results are very close to the experimental values provided by the manufacturers.

Tin-based perovskite solar cells (T-PSCs) are introduced as the next generation of valid and environment-friendly photovoltaic (PV) cells for near-future commercialization. However, there are some issues limiting T-PSCs including their instability, low efficiency, and use of toxic processing solvents. Among all these barriers, instability and early aging under thermal stress conditions are considered as significant challenges to the development of the T-PSCs. In this study, the impact of different temperature levels on the performance of a T-PSC is investigated over time. It is observed that early degradation of the device occurs at higher temperatures. For timely detection of the early aging, an accurate adaptive estimation of the series resistance is obtained in the equivalent single-diode circuit model of the T-PSC. It is shown that the trend of changes in the series resistance is a reliable indication of the aging process in the T-PSC. Finally, a mathematical index is derived for early aging detection based on the relative variation of the gradient from its minimum value in the linear regression analysis. The proposed approach could be utilized for timely detection of early aging conditions and protection of the device from permanent damage.

• An in-depth investigation for modeling and forecasting grid-connected PERC PV string performance has been conducted. • New mathematical models for DM and BM shape parameters as functions of temperature and irradiance have been introduced. • Novel analytical formulas relating DM shape parameters to key PV metrics have been derived. • Proposed models have been validated using real-world data of grid-connected PERC PV string serving in Benguerir, Morocco. • A strong agreement between predicted and measured PV performance has been demonstrated. This study focuses on modeling and forecasting the performance of a grid-connected photovoltaic (PV) string, aiming to enhance the accuracy of output power prediction, which is crucial for the effective management and stability of the electrical grid. While previous research has extensively examined PV modules, there is a notable lack of studies specifically targeting PV string behaviors, especially in harsh climates prevailing in semi-arid regions. This study addresses this gap by investigating the modeling and performance prediction of a Passivated Emitter Rear Cell (PERC) PV string under semi-arid climate conditions using analytical and predictive approaches based on both implicit and explicit models as Single Diode Model (SDM), Das Model (DM), Boutana et al. Model (BM) and Power Law Model (PLM). New mathematical models of DM and BM shape parameters versus temperature and irradiance along with novel analytical formulas giving DM shape parameters as a function of PV metrics have been introduced. The proposed approaches are validated using real measurements of a PERC PV string incorporating 12 JKM405M-72H PV modules operating at Green Energy Park (GEP) research facility in Morocco. The reliability of these approaches is assessed by comparing I-V curves, P-V curves and peak power generated and predicted by SDM, DM, BM and PLM to actual measurements. The comparison reveals that generated and predicted outcomes align well with measured data, with an average value of Normalized Root Mean Square Error (NRMSE) not exceeding 4.16 % for all models throughout the day. The findings indicate that the proposed approaches effectively predict the performance of the PERC PV string, accounting for climatic influences and providing insights into optimizing PV system performance, thereby contributing to improved grid stability in semi-arid regions. [ABSTRACT FROM AUTHOR]

The accurate model of the solar PV system is the principal organ that describes the performance of this resource. Several approaches based on optimizing algorithms were considered valuable tools to illustrate the I–V curve for improving the photovoltaic models. Their electrical parameters are estimated using optimization algorithms referring to the experimental database or manufacturer's datasheet. This paper proposes a novel developed a photovoltaic model based on improved arithmetic optimization algorithm (IAOA) to extract the solar cell parameters. Also, an experimental test bench is presented for obtaining the measured illustration of the I–V characteristics. Thus, the root mean square error value that describes the difference between measured and estimated results is considered the objective function for two different models, the simple-diode model and the one-diode model. The proposed IAOA results are compared with other research papers and optimization algorithms. Furthermore, the evaluation of the proposed IAOA has been discussed considering several statistical analysis tests. The presented results show that the effectiveness and accuracy of IAOA results are excellent, and their I–V characteristics coincide with experimental data. Moreover, the results obtained by the proposed algorithm show its high superiority in optimizing the solar cell parameters under a variety of operating conditions. [ABSTRACT FROM AUTHOR]

Potential-induced degradation (PID) in photovoltaic (PV) solar panels occurs due to the operation in strings that are part of large installations, and under determinate voltage and environmental operating conditions, especially humidity and temperature. The PID can cause decreasing of up to 40 % in the generated power capacity of the PV panel and, in the most severe cases, the end of its lifetime. When this phenomenon is detected in time, the causes can be corrected and, the effect on the PV panels could be susceptible to a reversibility process. This article presents a comparative analysis of the performance of four electrical indicators to detect PID reported in recent literature. This study is carried out by simulation, using the single-diode model to represent the PV panel, and under different irradiance and temperature conditions. The results show the advantages of an indicator based on normalized parallel resistance, in terms of its practicality and low sensitivity to changes in irradiance and temperature conditions. [ABSTRACT FROM AUTHOR]

The identification of parameters in solar cell models continue being an important issue in the simulation and design of the photovoltaic systems (PV). The models commonly used are based on diodes, the most important models are the three diode model; double diode model; and single diode model. Therefore, an optimization problem of the parameter extraction of these models can be treated with an objective function to minimize the difference between the calculated data and the measured data. In order to deal with parameter extraction in PV, several traditional numerical analytical and hybrid models have been developed. Recently, the meta-heuristic optimization algorithms (MHs) have been used to overcome the complex to find with proper accuracy, highly credible results quickly. Therefore, this paper introduces a modification of the basic three PV models and an innovative objective function is also considered. Moreover, a recent meta-heuristic algorithm called Heap-based optimizer (HBO) is applied for extracting the PV parameters of the traditional and the modified three PV models including three diode, double diode and single diode. Comparison between the traditional three photovoltaic models and the modified three photovoltaic models is performed in this work based on the innovative objective function. The experimental results revealed that the HBO superiority over other competitor algorithms. Based on the results, the values of estimated parameters that achieved by HBO are the optimal values with the smallest error between calculated data and measured data.

Renewable Energy (RE) has been rapidly growing day by day as a need of the world due to the energy crises and environmental effects. In recent years, the use of solar systems for the generation of electricity has gained considerable popularity. This increase mainly results from a scarcity of other energy sources, such as fossil fuels. As a result, there is a pressing need to transition to more dependable and long-term resources, such as photovoltaic (PV) systems. To improve the performance of PV proper design and development have been required for adequate extraction of their essential parameters. This study proposed the implementation and behaviour of a photovoltaic module (PVM) and describes the individual main equation situated on the Shockley diode to enable a detailed study of semiconductor physics and PV occurrence. The environmental performance of a PVM is represented using MATLAB which can be illustrative of the PVM for simple use in the simulation phase. The model was designed in MATLAB, an easy-to-use icon and dialog box that depends on the effect of solar radiation (SR) and cell temperature, output current (I) versus (vs) voltage (V), and power vs. Voltage. PVM is made with the simulated models are simulated and optimized. These models have been used to analyze the outcome of variations in various specifications on the PVM, including the operating temperature and the level of SR. The observed results have been compared with outcomes characteristics of PV, which are specified on the technical datasheet of the PVM. Simulation results have been obtained by using MATLAB software. From the results, it can be seen that at insolation 900W/m2 the output power of PVM is 280 but at 10°C the power of PVM is 270.

For a photovoltaic (PV) generator, knowledge of the parameters describing its equivalent circuit is fundamental to deeply study and simulate its operation in any weather conditions. In the literature, many papers propose methods to determine these parameters starting from experiments. In the most common circuit, there are five of these parameters, and they generally refer to specific weather conditions. Moreover, the dependence on irradiance and temperature is not investigated for the entire set of parameters. In fact, a few papers present some equations describing the dependence of each parameter on weather conditions, but some of their coefficients are unknown. As a consequence, this information cannot be used to predict the PV energy in any individual weather condition. This work proposes an innovative technique to assess the generated energy by PV modules starting from the knowledge of their equivalent parameters. The model is applied to a highly efficient PV generator with all-back contact, monocrystalline silicon technology, and rated power of 370 W. The effectiveness of the model is investigated by comparing its energy prediction with the value estimated by the most common model in the literature to assess PV energy. Generated energy is predicted by assuming PV power to be constant for a time interval of 1 min. [ABSTRACT FROM AUTHOR]

[Display omitted] • A neural network is used to detect faults in photovoltaic modules. • According to the shape of the current vs voltage curve, the faults are classified. • The training uses synthetic curves and the test is performed also on experimental data. • The points are normalized and resampled radially to improve the results. • With two hidden layers of 100 neurons each, the hit rate reaches more than 98.5%. In this paper a model-based procedure for fault detection and diagnosis of photovoltaic modules is presented. A four-layered feedforward artificial neural network learns the correlation between the features of the current vs. voltage curve and the environmental variables, which are the irradiance and the temperature. This correlation describes the behavior of the module at normal conditions. Moreover, the effect of anomalous variation of some parameters is learnt and correlated to the shape of the same curve, thus associated to a specific failure mechanism and to some assigned ranges quantifying the fault severity. The neural network is trained by using synthetic curves simulated by employing the single diode model and some well assessed and validated translation formulae. The obtained results over the simulated set of curves with different failures allow to achieve a classification error lower than 1.5%. The proposed approach has been also validated for detecting anomalous increases of the series resistance in a large experimental set of curves; in this case, a classification error of 2.7% has been achieved. [ABSTRACT FROM AUTHOR]

In this work, an efficient solution based on the reducing forms approach is presented to extract the five parameters of the single-diode model of PV generators from their I-V curves. Thus, by reducing the number of the five unknown parameters to two unknowns, the analytical expression of the current based on the LambertW function will then depend only on the ideality factor and the series resistance, as the two unknowns to predict numerically using the non-linear least square technique. The three other parameters are calculated as functions of the two predicted parameters using a linear system of three equations. Two sets of experiments are used for the validation of the proposed approach, which first showed its rapidity and high accuracy compared to the best approaches from the literature. Then, the method was applied for the real-time identification of four PV modules operating outdoors during one reference day at Cocoa (Florida). [ABSTRACT FROM AUTHOR]

To ensure the safety and stability of power grids with photovoltaic (PV) generation integration, it is necessary to predict the output performance of PV modules under varying operating conditions. In this paper, an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions. To study the dependence of the output performance on the solar irradiance and temperature, the proposed neural network model is composed of four neural networks, it called multi- neural network (MANN). Each neural network consists of three layers, in which the input is solar radiation, and the module temperature and output are five physical parameters of the single diode model. The experimental data were divided into four groups and used for training the neural networks. The electrical properties of PV modules, including I–V curves, P– V curves, and normalized root mean square error, were obtained and discussed. The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules. Compared with the traditional single-ANN (SANN) method, the proposed method shows better accuracy under different operating conditions.

In order to achieve the optimum feasible efficiency, the electrical parameters of the photovoltaic solar cell and module should always be thoroughly researched. In reality, the quality of PV designs can have a significant impact on PV system dynamic modeling and optimization. PV models and calculated parameters, on the other hand, have a major effect on MPPT and production system efficiency. Because a solar cell is represented as the most significant component of a PV system, it should be precisely modeled. For determining the parameters of solar PV modules and cells, the Chaos Game Optimization (CGO) method has been presented for the Single Diode Model (SDM). A set of the measured I-V data has been considered for the studied PV design and applied to model the RTC France cell, and Photowatt-PWP201 module. The objective function in this paper is the Root Mean Square Error (RMSE) between the measured and identified datasets of the proposed algorithm. The optimal results that have been obtained by the CGO algorithm for five electrical parameters of PV cell and model have been compared with published results of various optimization algorithms mentioned in the literature on the same PV systems. The comparison proved that the CGO algorithm was superior.

Building an accurate mathematical model of photovoltaic modules is an essential issue for providing reasonable analysis, control and optimization of photovoltaic energy systems. Therefore, this study provides a new accurate model of photovoltaic Panels based on single diode Model. In this case, the proposed model is the link between two models which are the ideal model and the resistance network. All parameters are estimated based on hybrid Analytical/Numerical approach: three parameters photocurrent, reverse saturation current and ideality factor are obtained using an Analytical approach based on the datasheet provided by the manufacturer under Standard Test Conditions. The series and shunt resistances are obtained by using a Numerical approach similar to the Villalva's method in order to achieve the purpose of modeling the resistance network part. Our model is tested with data from the manufacturer of three different technologies namely polycrystalline, Mono-crystalline silicon modules and thin-film based on Copper Indium Diselenide, and for more accurate performance evaluation we are introducing the Average Relative Error and the Root Mean Square Error. The simulated Current-Voltage and Power-Voltage curves are in accordance with experimental characteristics, and there is a strong agreement between the proposed model and the experimental characteristics. The computation time is 0.23 s lower than those obtained using others approach, and all obtained results under real environment conditions are also compared with different models and indicated that the proposed model outperforms the others approach such as villalva’s and kashif’s method.

In this study, an accurate description of some of the variables affecting the generation of electrical energy from solar cell structures of the type Silicon was investigated, these variables were affected by the difference in the temperature of the surrounding medium from 20 to 80 degrees Celsius, taking into account the optical energy that was located between 300 to 850 watts. Per square meter It became clear from the results that the effect of temperature has a direct impact on each of the variables (open circuit, fill factor, maximum power, efficiency), where it turns out that the latter decreases with increasing the temperature of the photo cell. The increase in the temperature of the optical cell increases the short circuit current, and therefore we find an increase in the reverse saturation current with an increase in temperature, the above influences turned out to be negative values except for short circuit current values. It became clear through the study that the relative change was directly dependent on the change in temperature and thickness, which was within the limits (0.576 to 0.555) volt for open circuit, (0.769 to 0.606) fill factor, (1.59 to 1.04 %) maximum power efficiency, respectively, these values are somewhat similar to what is found in research and studies, as well as computer simulations which identify the single diode model for the solar cell. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is proposing a new approach for retrieving the five parameters of the single diode equivalent model (SDM) of photovoltaic cells/modules including the series and shunt resistances. The least square method is used as an error minimization technique for fitting the non-linear transcendental model equation of the solar panel to the measured I–V characteristics. Newton Raphson method is applied to solve the system of five non-linear equations which represent the error of each parameter. Initial guess values are calculated with an optimised algorithm depending on information extracted from the same measured data. MATLAB programming script was used in all implementation steps. This approach is useful for a wide variety of applications where the five SDM parameters have to be determined from the measured curves, particularly, for self-fabricated cells/modules or in case of no available datasheet. One of the strengths of this method is the higher level of accuracy because of the absence of mathematical simplifications and physical assumptions. The method was validated on different types of PV devices, including a crystalline silicon cell, a polycrystalline module, and an amorphous module by using measurement data obtained at a wide range of solar irradiance conditions and temperatures. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid algorithm called differential evolution with integrated mutation per iteration (DEIM) is proposed to extract five parameters of single-diode PV module model obtained by combining differential evolution (DE) algorithm and electromagnetic like (EML) algorithm. The EML algorithm's attraction repulsion idea is employed in DEIM in order to enhance the mutation process of DE. The proposed algorithm is validated with other methods using experimental I-V data. The results of presented method reveal that simulated I-V characteristics have a high degree of agreement with experimental ones. The proposed model has an average root mean square error of 0.062A, an absolute error of 0.0452A, a mean bias error of 0.006A, a coefficient of determination of 0.992, a standard test deviation around 0.04540, and 15.33sec as execution time. The results demonstrate that the proposed method is better in terms of the accuracy and execution time (convergence) when compared with other methods where provide less errors. [ABSTRACT FROM AUTHOR]