Photovoltaic Cell Parameters Research Articles

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

AbstractAccurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

PV parameters estimation using optimized deep neural networks

Estimating the parameters of a Photovoltaic (PV) cell is crucial, given the significant integration of the PV systems into electrical power systems. One of the primary challenges in the estimation of PV cell parameters is identifying a generalized method applicable to any PV system, irrespective of environmental variations and power ratings. This paper introduces a novel application of an optimized deep neural network designed to estimate the parameters of the PV systems across a range of temperatures, irradiance values, and PV module ratings. The network undergoes a training process by utilizing data obtained from the PV module block located within the Simulink library. In order to evaluate the effectiveness of the proposed methodology, the network is subjected to a series of assessments. These assessments encompass the utilization of PV cell data from the Simulink library, comparisons with recently developed methods, and practical evaluations using experimental PV cell data to estimate the PV cell parameters. The findings underscore the simplicity and precision of the proposed method across diverse PV cells.

Influence of the ideality factor on the series resistance for some analytical methods of parameters extraction of PV panels

This paper revisits classical problem of PV cell parameters extraction based only on three points of their characteristics. Given the dispersion and discrepancy of the results provided by the various extraction methods, the goal is to examine and compare some analytical methods for reconstruction of I-V curves while stressing in particular on the influence of the ideality factor on the series resistance. These methods are tested on two commercial types of PV panels: monocrystalline and polycrystalline ones. Among some results, the performed analysis has shown the profile of the series resistance with respect to the ideality factor as well as their influence on I-V characteristics. In addition, a contribution proposes an explicit expression that links the series resistance to the ideality factor derived from Cubas’ formula upon an approximation of Lambert W-function.

Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models

High performance solar photovoltaic models require precise knowledge of solar PV cell parameters. Numerous methods based on both deterministic and meta-heuristics have been developed for identifying solar cell parameters. However, the presented methods in the literature have a heavy computational load and limited ability to extract crucial parameters due to nonlinear dynamics of solar PV systems. In addition, because they rely on approximations to determine the objective function, the preceding state-of-the-art parameter estimation techniques do not provide accurate results. Thus, a novel chaos-inspired invasive weed optimization (CIIWO) has been developed for accurate solar PV system parameter estimation. Adding a chaotic map to IWO improves the performance of suggested method by expanding the search space globally. Moreover, to cope with the inadequacy in state-of-art objective functions, Newton Raphson approach has been combined with proposed CIIWO algorithm. The suggested approach for solar cell parametric identification has been tested on one-diode, two-diode, and three-diode models. By contrasting the outcomes with nine contemporary optimization strategies for parameter estimation, the superiority of the suggested algorithm has been demonstrated. Commercial PV cell RTC France has been used for the experimental validation. Comprehensive study of experimental data validates the efficacy and stability of the suggested algorithm.

Study of the Impact of the Magnetic Field on the Electrical Parameters of the Radial Junction Photovoltaic Cell under Monochromatic Illumination

The present work is a theoretical study of the radial junction photovoltaic cell subjected to a magnetic field under monochromatic illumination. From a three-dimensional description, new analytical expressions of photocurrent density, photovoltage and electric power as a function of the magnetic field are established and by simulation on the Mathcad 15 software, we extracted the different curves. These representations allowed a thorough investigation of the operation of this model of a photovoltaic cell. It appears from this study that the amplitudes of parameters such as photocurrent density, photovoltage and electrical power are strongly attenuated when the photovoltaic cell is in the presence of a magnetic field assumed to be uniform over the entire region. For a magnetic field of 0.35 mT and a diffusion length, the short-circuit current decreased by 59.75% and the opencircuit photovoltage by 9.32%.

Maximum Power Point Tracking Constraint Conditions and Two Control Methods for Isolated Photovoltaic Systems

A maximum power point (MPP) always exists in photovoltaic (PV) cells, but a mismatch between PV system circuit parameters, weather conditions and system structure leads to the possibility that the MPP may not be tracked successfully. In addition, the introduction of an isolation transformer into a basic PV system allows for moderate values of the converter duty cycle and electrical isolation. However, there is no comprehensive research on MPPT (maximum power point tracking) constraint conditions for different isolated PV systems, which seriously hinders the application of isolated PV systems and the development of a related linear control theory. Therefore, in this paper, the overall mathematical models of different isolated PV systems are first established based on the PV cell engineering model and the MPP linear model, and then, two sets of constraint conditions are found for the successful realization of MPPT. These MPPT constraint conditions (MCCs) describe in detail the direct mathematical relationships between PV cell parameters, weather conditions and circuit parameters. Finally, based on the MPP linear model and MCCs, two new MPPT methods are designed for isolated PV systems. Considering the MCCs proposed in this paper, a suitable range of load and transformer ratios can be estimated from the measured data of irradiance and temperature in a certain area, and the range of MPPs existing in PV systems with different structures can be estimated, which is a good guide for circuit design, theoretical derivation and product selection for PV systems. Meanwhile, comparative experiments confirm the rapidity and accuracy of the two proposed MPPT methods, with the MPPT time improving from 0.23 s to 0.03 s, and they have the advantages of a simple program, small computational volume and low hardware cost.

A Comparative Study on Accurate Parameter Estimation of Solar Photovoltaic Models Using Metaheuristic Optimization Algorithms

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models.

Simultaneous inclusion of quantum dots in multi-functional layers of thin film organic solar cells

The role of quantum dot (QD) decoration in the hole transport buffer layer and the photoactive medium on the photovoltaic parameters of thin film organic solar cells (TFOSCs) was investigated. A cadmium–tellurium-based QD was synthesized successfully and embedded in two of the functional layers of a TFOSC to improve its overall power conversion efficiency. The experimentally determined optimum concentration of the QD was maintained in the interfacial layer to investigate the effect of QD concentration in the active layer. The observed increased short-circuit current density (Jsc) and open circuit voltage (Voc) are attributable to the enhanced energy level tuning, broadened optical absorption, and charge transport process facilitated by the integration of QDs inside the media. Moreover, an improved device efficiency was obtained when the solvent additive was introduced into the bulk heterojunction photoactive layer films to facilitate QD dispersion and increase the interpenetrating network of the active layer blend that reduces the occurrence of trap sites, which, in turn, limits the Auger recombination rates. The QD-doped TFOSCs catalyzed with solvent additives displayed an enhanced overall photovoltaic parameter, which is quite appreciable in comparison with that of the pristine devices.

High-Performance Technique for Estimating the Unknown Parameters of Photovoltaic Cells and Modules Based on Improved Spider Wasp Optimizer

To better estimate the unknown parameters of the double-diode model, a new optimization technique based on the newly proposed spider wasp optimizer (SWO) is introduced in this study. The performance of SWO was further enhanced by integrating it with a local search strategy to propose a new improved variant called ISWO. This improved variant has a high ability to extensively exploit the solutions surrounding the best-so-far solution in an effort to speed up convergence and produce better results in fewer function evaluations. Using the RTC France solar cell and three PV modules (STM6-40/36, STP6-120/36, and Kyocera KC200GT), ISWO and SWO are evaluated and compared to four well-known metaheuristic optimization methods. The objective values acquired by those algorithms in thirty separate runs are examined using the Wilcoxon rank sum test and a number of performance measures. The experimental findings demonstrate ISWO's exceptional performance for every PV module under consideration.

Intrinsic Photoconductivity Spectral Dependence as a Tool for Prediction of Open-Circuit Voltage in Organic Solar Cells

Organic materials are known for their variety of molecules. Methods to predict the parameters of organic photovoltaic (OPV) cells are required to avoid the time- and resource-consuming processes of manufacturing and testing OPVs. Usually, the open-circuit voltage (Uoc) is estimated as the difference between the ionization energy level of the electron donor molecule (Id) and the electron affinity level of the electron acceptor molecule (EAa). Various measurement methods are used to determine the energy level values of pure materials, which, when combined with energy level shifts due to the donor:acceptor interactions, make these estimations less precise. In this work, photoconductivity measurements were applied to the donor:acceptor films. Near threshold energy, the electron can be directly transferred from the donor to the acceptor molecule. The obtained charge transfer energy (ECT) shows the difference between Id and EAa in the film. This difference was compared to the Uoc value of an OPV made of the same donor:acceptor combination. We show that this approach provides less scattered results and a higher correlation coefficient compared to the Uoc estimation using energy level values.

Assessing the performance of MoTe2 based solar cell with Cu2O hole transport layer through device simulation

In this study, the SCAPS-1D tool has been used to numerically examine the performance of Transition Metal Dichalcogenides (TMDC) based Molybdenum ditelluride (MoTe2) solar cells containing CdS electron transport layer (ETL) and Cu2O hole transport layer (HTL). Based on the photovoltaic cell parameters, including absorber layer thickness, temperature, defect density, the effects of series and shunt resistance, and electron affinity, the structure of both MoTe2 based solar cells with and without the usage of the HTL has been analyzed. With 1.1 μm thickness of MoTe2 and doping density of 5 × 1015 cm−3, Al/FTO/CdS/MoTe2/Cu2O/Ni heterojunction’s solar cell proposed structure has been optimized. The final power conversion efficiency (PCE) = 32.38%, open-circuit voltage (Voc) = 1.07 V, short-circuit current (Jsc) = 35.12 mA/cm2, and fill factor (FF) = 86.32% has been determined from the optimized structure. The determined results indicate a suitable path for the realization of low cost and high efficiency MoTe2-based solar cell.

Impact of absorber layer thickness and defect density on the performance of MAPbI3 solar cells based on CuO2 as hole transport material

Perovskite solar cells are a promising next-generation solar energy harvester because of their excellent photovoltaic performance and simple fabrication procedure. The high power conversion efficiency of this cell, together with its low cost of materials and processes, differentiates it from commercial silicon or other organic and inorganic solar cells. In this work, numerical simulation was performed using Solar Cell Capacitance Simulator (SCAPS)−1D for the device structure (FTO/HTM/ CH3NH3PbI3/ETM/Au) to analyze the impact of absorber layer thickness and defect density on various parameters of Photovoltaic cells. The purpose of this research was to explore the properties of CH3NH3PbI3-based solar cells with different HTM layers, such as Cu2O and ETM layers, such as SnO2, TiO2, and ZnO, respectively. Additionally, the thicknesses of the perovskite absorber are adjusted to obtain the highest photovoltaic efficiency, and the impact of the defect at the perovskite absorber layer on solar cell performance is also investigated. According to the findings of this article, the thickness of the absorber layer and defect density in a perovskite solar cell had a significant impact on JSC, FF, VOC, and efficiency. The findings show a substantial gain in efficiency (18%) when the ETM layer is ZnO. As the defect density of the absorber layer increases, the JSC, VOC, FF, and PCE values of perovskite solar cells decrease considerably. According to our findings, the ZnO as ETM is the most likely to provide a high photovoltaic (PV) efficiency when combined with Cu2O-based HTM.

Parameter Estimation Techniques for Photovoltaic System Modeling

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells.

Comparative analysis of the photovoltaic cell parameters of dye-sensitized solar cells with composite photoanodes: Effect of the alien component

The synthesis of uniform PbOx-Fe2O3 nanoparticles (NPs) was carried out and mixed with commercial P25 titanium dioxide (TiO2) to fabricate a composite photoanode for enhancing adsorption and dye loading capabilities in dye-sensitized solar cells (DSSCs). The resulting composite photoanode exhibited a broader absorption band than the photoanode composed solely of pure TiO2. Amongst the different concentrations of PbOx-Fe2O3 NPs tested, the composite photoanode containing 0.1 wt% of NPs demonstrated the highest power conversion efficiency of 5.03%. It was noticed that the resistive loss was reduced in the composite photoanode, which is beneficial for minimizing energy losses within the device. However, the presence of conductive paths introduced by the NPs increased recombination and current leakage losses. A trade-off between these two types of losses needs to be carefully managed to maximize the overall performance of the DSSCs.

Using the snake optimization metaheuristic algorithms to extract the photovoltaic cells parameters

The accurate extraction of photovoltaic cells and panels parameters is very important in order to precisely forecast their generated energy, to rapidly identify the maximum power point, both for the manufacturer in order to verify the quality control during the production process, and for the researchers to improve their quality, efficiency and also study the photovoltaic cells degradation. This paper is the first to propose the usage of the Snake optimization metaheuristic algorithms to extract the parameters of three photovoltaic cells: monocrystalline commercial silicon, amorphous silicon, and RTC France. The Snake algorithms are used for the one diode and two diode models to extract five and seven parameters respectively. The root mean square error statistical test has been performed in order to prove the algorithm performance. The comparison with the algorithms published in specialized literature reveals that the Snake algorithm outperforms several others, but is not the best. An improved version is developed and the results show that it gives better results than, or at least the same as the best ones. The improvement in root mean square error, when the improved snake algorithm is used, is 16% in the case of the monocrystalline silicon photovoltaic cell for the two diode model, 5.3% in the case of the amorphous silicon photovoltaic cell for the two diode model, and it is slightly better for the RTC France photovoltaic cell, from 0.002 to 0.11% when the best algorithms are considered. The Snake algorithms give very good results, especially the improved one, even for a small population size and a limited number of iterations. Thus, for the improved snake algorithm, the iterations number and the population size are 100 and 80, in the case of the monocrystalline photovoltaic cells, for one diode model, and 200 and 40 respectively for two diode model, 100 and 120 in the case of the amorphous silicon photovoltaic cell for one diode model and 120 and 120 respectively for two diode model, 150 and 100 in the case of RTC photovoltaic cell for one diode model and 150 and 80 respectively for two diode model. This reduces the computational time for the extraction of the photovoltaic cell parameters. The computational time for the improved snake algorithm is half of the necessary one for one of the best algorithms, the hybrid successive discretization algorithm. Therefore, the snake optimization algorithm, especially the improved one is one of the best algorithms that can be used for the parameters extraction of the photovoltaic cells.

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate.

Optimal Identification for Dynamic PV Cell Parameter Based on a Data-Extension-Driven Method

Affected by environmental factors, equipment aging, operating status, etc., the parameters of photovoltaic (PV) models will deviate from the original setting parameters. In order to accurately identify the dynamic parameters of photovoltaics under the general simulation model, traditional parameter identification methods mainly use heuristic intelligent optimization algorithms for direct solution. Due to the limited data collected and the strong randomness of the algorithm, it is easy to make the identification accuracy and stability of photovoltaic parameters difficult to meet the requirements. To this end, this paper proposes an optimal identification method for PV dynamic parameters driven by data expansion. Firstly, the PV external characteristic data is fitted and generalized, which used the generalized regression neural network (GRNN). Then, the extended high-quality data can be used for dynamic parameter identification for PV cell. To confirm the performance of the proposed algorithm in this paper, this paper expands based on the actual external characteristic data of different proportions and uses the general PV simulation model to conduct comparative tests on various commonly used algorithms. The case studies under different scenarios show that the proposed algorithm can provide a more reliable and well-represented fitness function to the metaheuristic algorithms. Therefore, the optimization accuracy and stability of the proposed algorithm for dynamic PV cell parameter identification can be significantly improved simultaneously.

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

Accurate parameter estimation of photovoltaic (PV) cells is crucial for establishing a reliable cell model. Based on this, a series of studies on PV cells can be conducted more effectively to improve power output; an accurate model is also crucial for the operation and control of PV systems. However, due to the high nonlinearity of the cell and insufficient measured current and voltage data, traditional PV parameter identification methods are difficult to solve this problem. This article proposes a parameter identification method for PV cell models based on the radial basis function (RBF). Firstly, RBF is used to de-noise and predict the data to solve the current problems in the parameter identification field of noise data and insufficient data. Then, eight prominent meta-heuristic algorithms (MhAs) are used to identify the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) parameters of PV cells. By comparing the identification accuracy of the three models in two datasets in detail, the final results show that this method can effectively achieve parameter extraction, with advantages such as high extraction accuracy and stability, greatly improving the accuracy and reliability of parameter identification. Especially in the TDM, the I-V data and P-V data obtained from the PV model established through the identified parameters have very high fitting accuracy with the measured I-V and P-V data, reaching 99.58% and 99.65%, respectively. The research can effectively solve the low accuracy problem caused by insufficient data and noise data in the traditional method of identifying PV cells and can greatly improve the accuracy of PV cell modeling. It is of great significance for the operation and control of PV systems.

Space‐ and Post‐Flight Characterizations of Perovskite and Organic Solar Cells

Irradiation-Dependent Photovoltaic Parameters In article number 2300043, Peter Müller-Buschbaum and co-workers present a detailed analysis of irradiation-dependent photovoltaic parameters of perovskite and organic solar cells exposed to space conditions. The post-flight characterization elucidates the impact of sole transport versus rocket launch, space flight, and reentry stress.

Influence of Al doping in zinc oxide electron transport layer for the degradation triple-cation-based organometal halide perovskite solar cells

Various strategies have been adapted to fabricate stable organic-inorganic hybrid perovskite (PVT) solar cells (PSCs). The triple-cation (CH3NH3+ (MA+), CH3(NH2)2+ (FA+), and Cs+) along with dual-anion (I− and Br−)-based PVT (TC-PVT) layer offers better stability than single cation-based PVTs. The deprivation of the PVT absorber is also influenced by the interface of the absorber with the charge transport layer (electron transport layer (ETL) and hole transport layer (HTL)). Here, the degradation of the TC-PVT coated on Al-doped zinc oxide (AZO) as well as FTO/AZO/TC-PVT/HTL structured PSC was examined for various Al to Zn molar ratio (RAl/Zn) of AZO. The PL decay study of FTO/AZO/TC-PVT revealed that the lowest degradation in the power (35.38%) was observed for the AZO with RAl/Zn of 5%. Furthermore, the PV cell parameters of the PSCs were analytically determined to explore the losses in the PSCs during degradation. The shunt resistance reduction was maximum (50.32%) for RAl/Zn = 10%, whereas, minimum shunt loss (7.33%) for RAl/Zn of 2%. The highest loss due to series resistance was observed for RAl/Zn of 0%. The changes in diode ideality factor (n) and reverse saturation current density (J0) were the smallest for RAl/Znof 10%.