Photovoltaic Cell Parameters Research Articles

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

Hybrid of imperialist competitive algorithm and particle swarm optimization for parameter extraction of photovoltaic cells

To improve the efficiency of photovoltaic systems, it is essential to obtain the parameters of photovoltaic cells through an identification process. However, due to the nonlinear and multimodal characteristics, accurately and reliably identifying the parameters of photovoltaic cells still remains a challenging task. In this paper, a hybrid of the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), ICA-PSO, is proposed to effectively identify the parameters of photovoltaic cells. The position updating strategy of PSO is adopted to replace the colony’s position updating strategy in the ICA. The hybrid algorithm ICA-PSO integrates the multi-swarm search characteristic and the powerful exploration ability of PSO together, leading to an enhanced optimization performance. Experimental results of applying ICA-PSO to parameter identification of photovoltaic cells show that ICA-PSO can extract the parameters of photovoltaic cells with higher accuracy and reliability, thus outperforming many other methods presented in the literature.

Identifying the parameters of photovoltaic cells using Gaussian bare-bone imperialist competitive algorithm with opposition-based learning mechanism

Extracting the precise parameters of photovoltaic (PV) cells has become very important for simulation, evaluation, control, and optimization of PV systems. However, it is still a challenging task to accurately and reliably extract the parameters of PV cells. To solve this difficult problem, in this paper, a new meta-heuristic algorithm called Gaussian bare-bone imperialist competitive algorithm with opposition-based learning (OBL-GBBICA) is proposed to extract the parameters of PV cells. To strengthen the exploring ability and speed up the convergence, opposition-based learning (OBL) is introduced into an imperialist competitive algorithm (ICA) for two considerations. First, OBL is adopted in the population initialization to produce a high-quality population. Second, the OBL is introduced into the assimilation step to guide ICA to explore more promising regions. The above improvements not only speed up the convergence of ICA but also enhance its searchability, which is beneficial to improving the accuracy and reliability of identification results. Experimental results show that OBL-GBBICA exhibits great superiority in extracting the PV cells parameters, compared with other methods in the literature.

Machine learning-assisted SCAPS device simulation for photovoltaic parameters prediction of [formula omitted] perovskite solar cells

Perovskite solar cells (PSCs) have garnered significant research attention because of their remarkable surge in power conversion efficiency (PCE) surpassing 26% within a short timeframe. However, the widespread adoption of these highly efficient PSCs is hampered by the inherent toxicity associated with lead (Pb)-based perovskites, which currently dominate the field. Researchers have explored alternative cations like Ge2+ and Sn2+ Owing to their identical oxidation states to Pb2+, paving the way for lead-free PSC development. By examining the critical factors influencing CsSnI3 thin film solar cells’ performance as well as their interdependencies, this work seeks to improve the efficiency of these devices. To do so, machine learning algorithms (ML) are employed to figure out the critical factors influencing the completion of CsSnI3 solar cells. A new dataset of 7870 data points is generated through simulations with SCAPS-1D to effectively depict the perovskite solar cell. Five ML regression algorithms are thereafter utilized to predict the photovoltaic parameters of solar cells, such as the fill factor (FF), open circuit voltage (VOC), short circuit current (JSC), and PCE. The study shows that random forest (RF) and decision tree (DT) are the best performing algorithms, among which DT produces high accuracy and low error for predicting the PCE with a coefficient of determination R2 of 0.99, Pearson’s coefficient (Rvalue) of 0.99, Spearman’s correlation coefficient (ρ) of 0.99 and root mean square error of 0.08 on the testing set. Finally, this study offers valuable guidance for further experiment optimization by shedding light on the optimal device dimensions and essential components.

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

AbstractThe escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

A combined DFT and finite difference simulation study on hybrid halide perovskite-based solar cells

This work reports the modelling and numerical estimation of the photovoltaic parameters of perovskite solar cells (PSCs) containing formamidinium lead iodide (FAPbI3) and formamidinium tin iodide (FASnI3) in the form of light active materials using SCAPS 1D software. The analysis is done by introducing diverse hole and electron transport materials and back metal contacts to identify the most appropriate device configuration that can deliver optimum photovoltaic output. The optoelectronic properties and the absorption spectra of the two compounds are estimated by DFT calculation using WIEN2k, which incorporates density functional theory (DFT) principles and are provided as input to SCAPS 1D to improve the accuracy of modelling study. For FAPbI3 as absorber material, the configuration FTO/IGZO/FAPbI3/NiO/Au shows the highest photovoltaic parameter exhibiting a power conversion efficiency (PCE) and fill factor (FF) of 19.75 % and 74.29 % respectively. With FASnI3 as the absorber material, FTO/PCBM/FASnI3/P3HT/Au gives a PCE and FF of 21.6 % and 67.74 % respectively. The work also includes the analysis of influence of defect density at the interface layers and series and shunt resistance on performance of above-mentioned device architectures to identify their limitations under real experimental conditions. The influence of various attributes of perovskite on the performance of the device is examined to determine their optimum values.

Discussion On Three Optimization Plans for Photovoltaic Systems

In the context of the global energy structure, photovoltaic (PV) generation plays a critical role in reducing greenhouse gas emissions and improving energy sustainability. However, PV systems differ significantly from conventional power generation like coal and nuclear power due to their dependence on environmental factors such as weather and seasons, resulting in significant load fluctuations. To mitigate this issue, the optimization of PV systems is widely accepted, involving techniques such as energy storage and performance enhancement. Three methods are proposed to address the issue of power instability in photovoltaic systems: changing the tilt angle between photovoltaic panels and sunlight, changing the electrical parameters of photovoltaic cells, and compensation using liquid air energy storage technology. Based on these three methods, corresponding implementation directions and applicable ranges are provided for different needs. The advantages and disadvantages are also discussed.

Study of the Photovoltaic Parameters of Inorganic Solar Cells Based on Cu2O and CuO

Study of the Photovoltaic Parameters of Inorganic Solar Cells Based on Cu2O and CuO

A Fractional Order-Kepler Optimization Algorithm (FO-KOA) for single and double-diode parameters PV cell extraction

The primary objective of this study is to investigate the effects of the Fractional Order Kepler Optimization Algorithm (FO-KOA) on photovoltaic (PV) module feature identification in solar systems. Leveraging the strengths of the original KOA, FO-KOA introduces fractional order elements and a Local Escaping Approach (LEA) to enhance search efficiency and prevent premature convergence. The FO element provides effective information and past expertise sharing amongst the participants to avoid premature converging. Additionally, LEA is incorporated to boost the search procedure by evading local optimization. The single-diode-model (SDM) and Double-diode-model (DDM) are two different equivalent circuits that are used for obtaining the unidentified parameters of the PV. Applied to KC-200, Ultra-Power-85, and SP-70 PV modules, FO-KOA is compared to the original KOA technique and contemporary algorithms. Simulation results demonstrate FO-KOA's remarkable average improvement rates, showcasing its significant advantages and robustness over earlier reported methods. The proposed FO-KOA demonstrates exceptional performance, outperforming existing algorithms by 94.42 %–99.73 % in optimizing PV cell parameter extraction, particularly for the KC200GT module, showcasing consistent superiority and robustness. Also, the proposed FO-KOA is validated of on SDM and DDM for the well-known RTC France PV cell.

Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm.

Predicting the system efficiency of green energy and developing forward-looking power technologies are key points to accelerating the global energy transition. This research focuses on optimizing the parameters of proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells using the honey badger algorithm (HBA), a swarm intelligence algorithm, to accurately present the performance characteristics and efficiency of the systems. Although the HBA has a fast search speed, it was found that the algorithm's search stability is relatively low. Therefore, this study also enhances the HBA's global search capability through the rapid iterative characteristics of spiral search. This method will effectively expand the algorithm's functional search range in a multidimensional and complex solution space. Additionally, the introduction of a sigmoid function will smoothen the algorithm's exploration and exploitation mechanisms. To test the robustness of the proposed methodology, an extensive test was conducted using the CEC'17 benchmark functions set and real-life applications of PEMFC and PV cells. The results of the aforementioned test proved that with regard to the optimization of PEMFC and PV cell parameters, the improved HBA is significantly advantageous to the original in terms of both solving capability and speed. The results of this research study not only make definite progress in the field of bio-inspired computing but, more importantly, provide a rapid and accurate method for predicting the maximum power point for fuel cells and photovoltaic cells, offering a more efficient and intelligent solution for green energy.

Effective Non-Radiative Interfacial Recombination Suppression Scenario Using Air Annealing for Antimony Triselenide Thin-Film Solar Cells.

Antimony triselenide (Sb2Se3) has become a very promising candidate for next-generation thin-film solar cells due to the merits of their low-cost, low-toxic and excellent optoelectronic properties. Despite Sb2Se3 thin-film photovoltaic technology having undergone rapid development over the past few years, insufficient doping concentration and severe recombination have been the most challenging limitations hindering further breakthroughs for the Sb2Se3 solar cells. Post-annealing treatment of the Sb2Se3/CdS heterojunction was demonstrated to be very helpful in improving the device performance previously. In this work, post-annealing treatments were applied to the Sb2Se3/CdS heterojunction under a vacuum and in the air, respectively. It was found that compared to the vacuum annealing scenario, the air-annealed device presented notable enhancements in open-circuit voltage. Ultimately a competitive power conversion efficiency of 7.62% was achieved for the champion device via air annealing. Key photovoltaic parameters of the Sb2Se3 solar cells were measured and the effects of post-annealing treatments using different scenarios on the devices were discussed.

Improving photovoltaic cell parameter calculations through a puffer fish inspired optimization technique

The precise estimation of solar PV cell parameters has become increasingly important as solar energy deployment expands. Due to the intricate and nonlinear characteristics of solar PV cells, meta-heuristic algorithms show greater promise than traditional ones for parameter estimation. This study utilizes the Puffer Fish (PF) meta-heuristic optimization method, inspired by male puffer fish's circular structures, to estimate parameters of a modified four-diode PV cell. The PF algorithm's performance is assessed against ten benchmark test functions, with results presented as mean and standard deviation for validation. Comparative analysis with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS) algorithms highlights PF's superior performance, achieving optimal solutions with minimal error of 7.8947E-08. Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirm PF's superiority. The circular structures of male puffer fish serve as an effective model for optimization algorithms, enhancing parameter estimation. Benchmark tests and statistical analysis consistently underscore PF's superiority over other meta-heuristic algorithms. Future research should explore PF's potential applications in solar energy and beyond.

Identification of PV Parameters Based on Bézier Curve and AWSO

AbstractTo 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.

Chaotic Generator and Artificial Ecosystem-Based Optimization for Accurate Modeling of Photovoltaic Cell Parameters

Chaotic Generator and Artificial Ecosystem-Based Optimization for Accurate Modeling of Photovoltaic Cell Parameters

Effect of Gamma Irradiation on Electrophysical Parameters of Silicon Solar Cells Doped with Nickel

The results of the studies of changes in the electro-physical parameters (Voc – no-load voltage, Isc – short-circuit current density; τ – lifetime of nonequilibrium charge carriers) of photocells made on plates of monocrystalline silicon of the p-type conductivity with the specific resistance ρ 0.5 Ohm cm, doped with nickel, under irradiation with γ-quanta from 60Co source are presented. It is shown that the efficiency of the solar energy conversion in nickel-doped photovoltaic cells remains higher than in standard cells up to irradiation doses of 108 rad. It was established that with increasing diffusion temperature of nickel atoms radiation stability of electrophysical parameters of photovoltaic cells also increases. A decrease in the concentration of recombination-active radiation defects is due to the getterization by nickel atoms of technological (background) impurities and the action of nickel clusters as effluents for radiation-induced vacancies.

Improving effects of 1-naphthalenetic acid (NAA) as a co-adsorbent on photovoltaic parameters of dye-sensitized solar cells

Improving effects of 1-naphthalenetic acid (NAA) as a co-adsorbent on photovoltaic parameters of dye-sensitized solar cells

A novel optimal identification of various solar PV cell parameters by using MRDT controller

At present, Renewable Energy Sources (RES) utilization keeps on increasing because of their merits are more availability in the atmosphere, easy energy harvesting, less maintenance expenses, plus more reliability. Here, the solar power generation systems are utilized for supplying the energy to the local consumers. The accurate, and efficient solar power supply to the customers is a very important factor to meet the peak load demand. The accurate power generation of the sunlight system completely depends on its accurate parameters extraction. In this work, a Modified Rao-based Dichotomy Technique (MRAODT) is introduced to identify the actual parameters of the different PV cells which are PWP 201 polycrystalline, plus RTC France. The proposed MRAODT method is compared with the other existing algorithms which are the teaching and learning algorithm, African vultures, plus tuna intelligence algorithm. Finally, from the simulation results, the MRAODT gives superior performance when associated with the other controllers in terms of parameters extraction time, accuracy in the PV cells parameters identification, plus convergence time of the algorithm.

Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems

The characteristic parameters of renewable energy sources (RESs) are changes over time under changing operating conditions. Therefore, the accuracy of the model is important in order to establish appropriate control and operating plans for the stable operation of such systems. Determining the parameters of RESs and creating an accurate model of them is an inevitable reality and plays an important role in the success of system modeling. Many methods have been used to determine these parameters in the literature. Furthermore, optimization algorithms are now considered to be among the most significant approaches for resolving problems pertaining to system parameter extraction in many scientific areas. In the study, this parameter extraction is done by a new optimization method called stochastic fractal search algorithm (FDB-NSM-SFS-OBLs), which includes natural survivor updating (NSM) and fitness distance balance (FDB) guiding mechanisms, and opposite based learning (OBL) methods. The performance of the proposed FDB-NSM-SFS-OBL algorithm has been tested in two different experimental studies. In the first experimental study, the proposed approach is tested on CEC2020 benchmark test functions and the algorithm structure containing the best OBL approach is determined using nonparametric Wilcoxon and Friedman statistical analysis methods. The second experimental study is carried out with the proposed optimization algorithm to estimate the parameters of the photovoltaic cell used in the modeling, which is the building block of photovoltaic panels used in renewable energy systems, proton-exchange membrane fuel cell (PEMFC) that is another renewable energy source, and Li-Ion battery used as a backup power unit. The FDB-NSM-SFS-OBL algorithm, which is used both in the CEC2020 benchmark test functions and in determining the parameters of photovoltaic (PV) cells, PEMFC and Li-Ion batteries, performed better in searching and finding the global solution point during the optimization process.

Effect of 1 wt% Dimethyl Sulfoxide on the Electrical and Photovoltaic Parameters of Schottky Diodes and Organic Solar Cells Based on PEDOT:PSS

Herein, the electrical conductivity of poly(3,4‐ethylenedioxythiophene)–poly(styrenesulfonate) (PEDOT:PSS) polymer is improved by adding dimethyl sulfoxide (DMSO) and electrical characterization of the obtained Schottky diode and organic solar cell (OSC) devices are performed from I–V measurements under the light and dark conditions. The scanning electron microscope images of the poly(3‐hexylthiophene):[6,6]‐phenylC61‐butyric acid methylester active layer and PEDOT:PSS layer are obtained. The series resistance (Rs), barrier height (Фb), and ideality factor (n) parameters are obtained from different techniques as a function of light and dark conditions. Furthermore, photovoltaic parameters are calculated for n‐Si/PEDOT:PSS/Au device without DMSO doping and with DMSO doping as a parameter of the light condition and the interface state densities of these devices are obtained for light and dark conditions. DMSO organic solvent improves the short‐circuit current density (Isc), power convert efficiency (or η), open circuit‐voltage (Voc), shunt resistance (Rsh), and series resistance (Rs) values of OSCs. In summary, DMSO solvent rearranges the conductive of PEDOT:PSS, decreases the work function of this conductive polymer (≈5 eV), decreases energy loss in the charge transportation, and increases photovoltaic parameters of Schottky barrier diode and OSC devices.

Investigation of Evolutionary Computation Techniques for Enhancing Solar Photovoltaic Cell Performance

The pursuit of optimized solar photovoltaic (PV) cell parameters is critical for advancing renewable energy technologies amidst global energy security and climate change challenges. This research investigates the efficacy of particle swarm optimization (PSO) and gray wolf optimization (GWO) in fine-tuning PV cell behavior parameters. Leveraging evolutionary computation, the study aims to maximize energy output, minimize costs, and enhance system reliability by optimizing material properties, structural configurations, and operating conditions. Through iterative optimization, PSO and GWO navigate the parameter space with precision, yielding solutions that maximize energy yield and system efficiency.