Double Diode Research Articles

Realization of a General Method for Extracting Specific Contact Resistance of Silicon‐Based Dopant‐Free Heterojunctions

Specific contact resistance (ρC) plays a significant role in determining the efficiency of dopant‐free heterojunction (DFHJ) silicon solar cells. Existing methods allow accurate measurement of ρC only in the majority carrier collection region. Herein, taking the heterojunctions of transition metal oxide/c‐Si(n) as an example, how to extract ρC from the minority carrier (hole) collection region by ingeniously using the expanded Cox and Strack method is demonstrated. On the basis of technology computer‐aided design and a double (diode + resistance) equivalent circuit model, the improved method can separate the electron current density and hole current density from each other and thus the corresponding resistance for both polarities can be well determined. The effectiveness of the improved method in precisely extracting the corresponding ρC is further verified by a series of experimental examples. A general method of unambiguously extracting specific contact resistance in DFHJ silicon solar cells is established.

Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Extracting the optimum parameters of solar photovoltaic (PV) model using the experimental data of current–voltage is very critical in simulating, controlling, and optimizing the PV systems. One of the important problems encountered in modeling and simulating is to find a model that can extract parameters from PV models quickly, accurately, and reliably. Based on this motivation, the goal of this study is to suggest an improved algorithm, namely genetic algorithm based on non-uniform mutation (GAMNU), in order to approximate efficiently the parameters of solar cells and PV modules. In GAMNU, non-uniform mutation operator is used to maintain diversity in the explored solutions and the crossover operator follows an adaptive search strategy, which consists of searching the entire space at the beginning while maintaining a focused search when the population tends to converge in a certain region of the search space. The performance of the method is comprehensively evaluated on different solar cell models, including single and double diode, and single diode PV modules, of a R.T.C France silicon solar cell, ESP-160 PPW PV, STP6-120/36 and Photowatt-PWP201 module. The results obtained from single and double diode models for R.T.C France are respectively 9.8618×10-4 and 9.8683×10-4, and for Photowatt-PWP201, STP6-120/36 and ESP-160 PPW are 2.3824×10-3 2.382420230900×10-3, 1.6735×10-21.6735786505085×10-2 and 8.2942×10-2 8.2942×10-2. The statistical obtained results show that the proposed method has very competitive performance in terms of accuracy and reliability when compared to other advanced algorithms. Therefore, the proposed algorithm is highly useful to extract the parameters of solar PV models.

An efficient tree seed inspired algorithm for parameter estimation of Photovoltaic models

Energy is one of the most important requirements for countries and serves as an indicator of the development level of a country. Developments in social, economic and industrial areas have increased the energy needs of countries. Solar energy is one of the most promising renewable energy sources to meet increased energy demands. Photovoltaic (PV) models used to generate electricity from solar energy are among the most popular renewable energy systems. To obtain maximum efficiency from PV models, it is very important to estimate parameters based on current–voltage data, simulate and control PV models for electricity generation. To achieve maximum efficiency for PV models, their parameters should be estimated in an optimum way. Therefore, tree seed algorithm (TSA) developed inspired by nature was proposed in this study. TSA is a meta-heuristic algorithm inspired by the connection between trees and seeds and proposed for solving continuous optimization problems. Parameters of three different PV models (single diode model, double diode model and PV module) were optimized with TSA. Various analyses were performed to prove the success of the proposed method in parameter optimization of PV models. The results were also compared with those of similar studies in the literature. The analysis results indicated that the proposed method yielded very successful, effective and reliable results, and it was concluded that it was a competitive method compared to other methods in the literature.

Multiple learning JAYA algorithm for parameters identifying of photovoltaic models

Extracting the optimum parameters of the solar photovoltaic (PV) model based on measured current–voltage data accurately, reliably, and quickly is a vital step to simulating, evaluating, and optimizing the model PV. Although several meta-heuristics have been proposed to extract photovoltaic parameters using experimental data, it suffers from slow convergence and poor efficiency which leads to poor solution quality. In this paper, a multiple learning JAYA (MLJAYA) approach has been proposed to extract the PV parameters under different operating conditions. The main advantage of multiple learning strategies introduced in the MLJAYA proposed is its ability to balance exploration and exploitation capabilities. The chaos perturbation mechanism is employed to improve the quality of the population to escape the local optimum and improve the accuracy of the basic JAYA algorithm. The structure of MLJAYA is very simple and requires only the size of the population and stop criterion. The efficacy of the proposed MLJAYA has been verified by including different PV modules models such as single diode, double diode, and PV module, and the results obtained was compared with different variants of JAYA and other state-of-the-art optimization algorithms. The best results of root mean square error (RMSE), and absolute error generated by MLJAYA are (RMSE = 9.8602E−04, SIAE = 1.781248E−02), and (RMSE = 9.8248E−04, SIAE = 1.76E−02) for RTC solar cell single diode and double diode respectively, and (RMSE = 2.4250748E−03, SIAE = 4.686375E−02) for Photowatt-PWP201. The results show the superior performance in terms of accuracy and reliability. Since, the MLJAYA show is a potential approach for extracting parameters from photovoltaic models.

Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer

The global trend towards renewable energy sources, especially solar energy, has had a significant impact on the development of scientific research to manufacture high-performance solar cells. The issue of creating a model that simulates a solar module and extracting its parameter is essential in designing an improved and high performance photovoltaic system. However, the nonlinear nature of the photovoltaic cell increases the challenge in creating this model. The application of optimization algorithms to solve this issue is increased and developed rapidly. In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this. In the literature, different PV models are presented, including static and dynamic PV models. Firstly, in order to evaluate the effectiveness of the proposed ESCGBO algorithm, it is executed on the 23 benchmark functions and the obtained results using the proposed algorithm are compared with that obtained using three well-known algorithms, including the original GBO algorithm, the equilibrium optimizer (EO) algorithm, and wild horse optimizer (WHO) algorithm. Furthermore, both of original GBO and developed ESCGBO are applied to estimate the parameters of single and double diode as static models, and integral and fractional models as examples for dynamic models. The results in all applications are evaluated and compared with different recent algorithms. The results analysis confirmed the efficiency, accuracy, and robustness of the proposed algorithm compared with the original one or the recent optimization algorithms.

Dual gate material (Au and Pt) based double-gate MOSFET for high-speed devices

Aluminium Gallium Arsenide (AlGaAs) is a semiconductor material used in the latest design of double heterostructure laser diodes. This semiconductor is mostly available in the arbitrary alloy form between Gallium Arsenide and Aluminium Arsenide. It is derived from the Tri-MethylGallium (TMG/TMGa), and Arsine (AsH3), both the chemicals are pyrophoric and toxic. The resistance is less between source and drain contacts in the case of AlGaAs so that it has been proposed as a material to grow contacts on Indium Phosphide (InP) layer. The AlGaAs uses an ion implantation model for a design purpose which lowers the thermal power while the operation of the device. The parasitic capacitance has to be taken care of while designing a device using this material since the capacitance affects much in the AlGaAs based devices. The average velocity of the electrons has been observed to be increased by 14.63 % in the Au-gate (gate-1) and Pt-gate (gate-2) material-based Double-Gate (DG) MOSFET compared to the Silicon-based DG MOSFET. This paves the way for higher electron mobility, in turn, it can be used in highfrequency device manufacturing. The proposed material can be used in high-speed hybrid applications such as HEMTs and radiofrequency devices for long-haul communication.

A novel levy flight trajectory-based salp swarm algorithm for photovoltaic parameters estimation

Simulation and optimization of photovoltaic (PV) systems are recently urging as a hotspot in the field of renewable energy. For this, different models have been introduced to simulate the behavior of real PV cells and modules. An accurate estimation of unknown parameters of PV models is necessary to achieve a good performance of photovoltaic systems. This paper presents a new metaheuristic algorithm called Levy flight trajectory-based salp swarm algorithm (LSSA), which estimated single diode (SD), double diode (DD) and PV module models parameters. Compared with the original salp swarm algorithm (SSA), LSSA benefits from enhanced population diversity due to Levy flight trajectory characteristic of long jump steps. In order to validate its efficiency, the proposed technique was compared with other well-known meta-heuristics for estimating PV parameters in the SD, DD and PV modules. The experimental and comparative results demonstrated that LSSA was able to find extremely accurate solutions with high reliability and offers very competitive results for the problem of estimating PV parameters.

Nature inspired evolutionary algorithm integrated performance assessment of floating solar photovoltaic module for low-carbon clean energy generation

Development and deployment of FSPV systems are still in the nascent stages hence long-term performance, control and feasibility study of FSPV systems are not well addressed. Precise and robust estimation of FSPV panel parameters will play an important role in determining the actual performance, carbon savings and long-term feasibility studies of FSPV systems. Here, hybrid stochastic firefly algorithm (HSFA) is used for parameter estimation and optimization. The hybrid algorithm is used to find out the parameters of single diode model, double diode model and FSPV module. An experiment is also performed using FSPV modules under varying irradiance conditions. The accuracy of model is evaluated by comparing the simulated results with the experimental results and computing the relative error and root mean square error (RMSE). The parameters extracted using the proposed method has a very low RMSE value of 9.83002E-04. Assessment of the experimental and estimated results show that the relative error for measured electricity on a sunny day is 0.57% while for an overcast day it is 0.89%. For partial shading condition, the relative error and RMSE was 0.79% and 6.5%, respectively. The results which are in well agreement with the experimental values demonstrate the superior performance of the model in determining the FSPV parameters. Proper estimation of the FSPV parameters will help researchers, scientists, engineers and all actors associated with solar PV systems in making sound judgements towards the deployment of FSPV systems and help the society in developing a sustainable ecosystem towards implementation of industry 4.0 by adapting to low-carbon power generation methods.

An evolutionary Nelder–Mead slime mould algorithm with random learning for efficient design of photovoltaic models

The efficiency of solar cells in converting solar energy into electrical energy can be improved by efficient and accurate solar photovoltaic cell modelling. However, the key to solar photovoltaic cell modelling is the accuracy of the solar cell parameters. Therefore, to obtain the unknown parameters of the solar cell accurately and efficiently, we proposed an improved slime mould algorithm (ISMA) combining the Nelder–Mead simplex (NMs) mechanism with a random learning mechanism. Specifically, the NMs mechanism ensures that the population is intensive and keeps moving closer to food as the population evolves. At the same time, the random learning mechanism incorporating the monitoring mechanism encourages optimal individuals to continuously learn the results of random communication among different agents and effectively improves the local search capability of the traditional SMA. To validate the performance of the proposed ISMA, it has been utilized to identify the optional parameters of a single diode, double diode, and PV modules. Based on experimental results, it is shown that ISMA outperforms most existing techniques in terms of convergence accuracy, convergence speed, and stability. In addition, ISMA also shows excellent stability in identifying the unknown parameters of three commercial photovoltaic modules under different environmental conditions. In summary, the proposed ISMA can be a promising technology in extracting the parameters of the photovoltaic models.

Particle swarm optimization approach to determine all parameters of the photovoltaic cell

In order to describe the behavior and the performance of the photovoltaic modules we can found in the literature different equivalent circuit models such us: single diode model (SDM), double diode model (DDM), three diode model (TDM). Almost of the authors obtained that the SDM is the best solution to describe the electrical characterization of the PV modules. The PV parameters can be extracted by three common approaches: Analytical based on the derivation of mathematical equations, Numeric or iterative usually use non-linear optimization techniques, among all the technique the Newton Raphson is widely used. The last one is meta-heuristic approach characterized by their global search point and their soft computing algorithms. The aim of this study is to explore and discuss the behavior of the single-diode model, which require five parameters are estimated using Particle Swarm Optimization approach. The performance of this method is evaluated using the experimental values of R.T.C France PV cell at irradiation G = 1000 W/m2 and at temperature T = 33 °C. The obtained results are also compared with results of the others computing approach. After this study, we can conclude that the proposed PSO method has the best performance because the RMSE obtained using this approach is around the 1.73.10−4 A and low than those obtained through all compared algorithms.

Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.

Comparative study with practical validation of photovoltaic monocrystalline module for single and double diode models

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module. “Matlab/Simulink” is used to model and simulate the PV models because it is considered a major software for modeling, analyzing, and solving dynamic system real problems. In this work, a new modeling method based on the “Multiplexer and Functions blocks” in the "Matlab/Simulink Library" is presented. The mathematical analysis of single and double diodes is conducted on the basis of their equivalent circuits with simple modification. The corresponding equations are built in Matlab by using the proposed method. The unknown internal parameters of the PV panel circuit are extracted by using the PV array tool in Simulink, which is a simple method to obtain the PV parameters at certain weather conditions. Double-diode model results are compared with the single-diode model under various irradiances and temperatures to verify the performance and accuracy of the proposed method. The proposed method shows good agreement in terms of the I–V and P–V characteristics. A monocrystalline NST-120 W PV module is used to validate the proposed method. This module is connected to a variable load and tested for one summer day. The experimental voltage, current, and power are obtained under various irradiances and temperatures, and the I–V and P–V characteristics are obtained.

PRODUCTION OF SOLAR CELLS AND ESTIMATION OF ITS ELECTRICAL PARAMETERS FROM ITS EXPERIMENTAL CHARACTERIZATION

In this work, solar cells with Mo/CuInSe2/CdS/n+–ZnO structure were deposited and experimentally characterized by the I-V curve. From these results, the electrical parameters were estimated fitting, theoretically, the curve to determine the most important variables. The estimation was carriedout using the single and double diode models and comparing the curve that best fits. The estimated variables were photogenerated current (Iph), dark current of the diode (Is), series resistance (Rs), shunt resistance (Rp) and diode ideality factors (a1 and a2) according to each model of circuit taken for optimization. The adjustment of the current-voltage (I-V) and power-voltage (P-V) curves achieved with the double diodemodel indicates that in the manufactured solar cells, interfacial states are present in the p-n junction, which decreases the efficiency of the device.

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

Fermi Velocity Modulation Induced Low‐Bias Negative Differential Resistance in Graphene Double Barrier Resonant Tunneling diode

AbstractNegative differential resistance (NDR) devices are adequate candidates for the functional devices applicable to the next‐generation integrated circuit technology so‐called “Beyond CMOS.” Here, a graphene velocity‐modulation‐barrier resonant‐tunneling diode operating at room temperature is proposed. The current–voltage characteristics of the device are analyzed using the non‐equilibrium Green's function technique. It is found that the Fermi velocity barrier in the well/barrier region manipulates the tunneling transmission probability by suppressing the Klein region and improving the resonant tunneling leading to NDR. For special values of velocity barriers, resonant states have maximum alignment with each other which increases peak current with a high peak to valley ratio (PVR). The width and the position of the NDR window are controlled and engineered by the device dimensions and the height of potential barriers. The smaller the device showed the better the NDR properties such as larger current density and maximum PVR. Taken together, the results reveal that adequate magnitude of the Fermi velocity in graphene barrier can be an impressive concept for the fabrication of emerging tunneling devices.

An efficient Equilibrium Optimizer for parameters identification of photovoltaic modules.

The use of solar photovoltaic systems (PVs) is increasing as a clean and affordable source of electric energy. The Pv cell is the main component of the PV system. To improve the performance, control, and evaluation of the PV system, it is necessary to provide accurate design and to define the intrinsic parameters of the solar cells. There are many methods for optimizing the parameters of the solar cells. The first class of methods is called the analytical methods that provide the model parameters using datasheet information or I–V curve data. The second class of methods is the optimization-based methods that define the problem as an optimization problem. The optimization problem objective is to minimize the error metrics and it is solved using metaheuristic optimization algorithms. The third class of methods is composed of a hybrid of both the analytical and the metaheuristic approaches, some parameters are computed by the analytical approach and the rest are found using metaheuristic optimization algorithms. Research in this area faces two challenges; (1) finding an optimal model for the parameters of the solar cells and (2) the lack of data about the photovoltaic cells. This paper proposes an optimization-based algorithm for accurately estimating the parameters of solar cells. It is using the Improved Equilibrium Optimizer algorithm (IEO). This algorithm is improved using the Opposition Based Learning (OBL) at the initialization phase of EO to improve its population diversity in the search space. Opposition-based Learning (OBL) is a new concept in machine learning inspired by the opposite relationship among entities. There are two common models for solar cells; the single diode model (SDM) and double diode model (DDM) have been used to demonstrate the capabilities of IEO in estimating the parameters of solar cells. The proposed methodology can find accurate solutions while reducing the computational cost. Compared to other existing techniques, the proposed algorithm yields less mean absolute error. The results were compared with seven optimization algorithms using data of different solar cells and PV panels. The experimental results revealed that IEO is superior to the most competitive algorithms in terms of the accuracy of the final solutions.

Actively Controlled Frequency-Agile Fano-Resonant Metasurface for Broadband and Unity Modulation

The active control to the local resonant mode of metasurface is a promising route for improving the operation bandwidth limitation of metasurface. Here, we propose and experimentally demonstrated the active tunabilities in a frequency-agile Fano-resonant metasurface. The metasurface with a pair of asymmetric split ring resonators is integrated with double varactor diodes for active control of the sharp Fano resonance. It is found that the sharp Fano-type spectrum appears due to the near-field interferences between the collective electric and magnetic dipole modes. The physical insight is revealed through local field analysis, multipole decomposition and temporal coupled-mode theory. It is also found that the metasurface can be employed as a broadband and unity modulator. Hopefully, our results could inspire sophisticated electrically controlled photonic devices with novel functions.

Efficient and continuous microwave photoconversion in hybrid cavity-semiconductor nanowire double quantum dot diodes

Converting incoming photons to electrical current is the key operation principle of optical photodetectors and it enables a host of emerging quantum information technologies. The leading approach for continuous and efficient detection in the optical domain builds on semiconductor photodiodes. However, there is a paucity of efficient and continuous photon detectors in the microwave regime, because photon energies are four to five orders of magnitude lower therein and conventional photodiodes do not have that sensitivity. Here we tackle this gap and demonstrate how microwave photons can be efficiently and continuously converted to electrical current in a high-quality, semiconducting nanowire double quantum dot resonantly coupled to a cavity. In particular, in our photodiode device, an absorbed photon gives rise to a single electron tunneling through the double dot, with a conversion efficiency reaching 6%.

Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules

How to effectively realize the simulation, evaluation, and control of the photovoltaic (PV) system established on the actual measured voltage and current PV cells and module data has attracted widespread attention. The original SE possesses the disadvantages of slow convergence and poor accuracy in the parameter identification of PV cells and modules. This paper proposes an enhanced spherical evolution algorithm (SE) based on a novel dynamic sine-cosine mechanism (DSCSE). The introduction of the dynamic sine-cosine mechanism significantly promotes the information communication of disparate individuals and increases the diversity of diverse populations. To assess the performance of DSCSE, it is compared with ten comparative algorithms to estimate unknown parameters of PV cell and module at fixed and varying temperature and light conditions, including single diode model (SDM), double diode model (DDM), three diode model (TDM) and PV module. The experimental results indicate that the root mean square of the error (RMSE) gained by DSCSE outperforms most competing algorithms. The results of RMSE by DSCSE for SDM, DDM, and TDM of commercial solar cells R.T.C. France and PV module of Photowat-PWP201 is the percentage of improvement of 48.45%, 6.85%, 11.81%, and 4.73% compared to SE, respectively. Furthermore, for three manufacturers, including Mono-crystalline (SM55), Thin-film (ST40), and Multi-crystalline (KC200GT), the results of RMSE by DSCSE harvest the maximum and minimum increase of 96.1% and 31.36%. Therefore, DSCSE is expected to become a novel promising technology to estimate the parameter of PV cells and modules.

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).