Diode Model Research Articles

Design of High Peak Power Pulsed Laser Diode Driver

This paper attempts to describe a laser diode driver circuit using the depletion mode gallium nitride high electron mobility transistor (D-mode GaN HEMT) to generate nanosecond pulses at a repetition rate up to 10 MHz from the vertical-cavity surface-emitting laser (VCSEL). The feature of this driver circuit is a large instantaneous laser power output designed in the most efficient way. The design specifications include a pulse duration between 10 ns and 100 ns and a peak power up to above 100 W. The pulsed laser diode driver uses the D-mode GaN HEMT, which has very small Coss difference between turn-on and turn-off states. The analysis is according to a laser diode model that is adjusted to match the VCSEL, made in National Yang Ming Chiao Tung University (NYCU). A design guide is summarized from the derivations and analysis of the proposed laser diode driver. According to the design guide, we selected the capacitor, resistor, and diode components to achieve 10 ns to 100 ns pulse duration for laser lighting. The experiment demonstrated that the maximum power-to-light efficiency can be as high as 86% and the maximum peak power can be 150 W, which matches the specifications of certain applications such as light detection and ranging (LiDAR).

An Efficient Heap-Based Optimizer for Parameters Identification of Modified Photovoltaic Models

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.

Obtention of solar cell parameters, through convergence of iterative cycles. Part 1: Theoretical analysis and cycles proposal

In this Part 1 of this series of articles, two iterative cycles are proposed to accurately determine the shunt resistance (Rsh), the series resistance (Rs), the ideality factor (n), the light current (Ilig), and the saturation current (Isat) of solar cells, within the one diode model. First, Rs and n are obtained linearly fitting ∂V∂lnI′vs.I′, where I′ is a new defined current I′=I+Isat+Ilig−V−IRsRsh+nkTRsh. Then, Rsh and Isat are obtained using Procedure A and B proposed in [2]. Once these four solar cell parameters are obtained, a correction to Ilig is deduced and applied. The deduction of these five solar cell parameters is reused to recalculate I′ and the iterative cycles are redone till some convergence criteria is achieved. The accuracy and number of cycles necessary to achieve reasonable results are tested and discussed on ideal (noiseless) current voltage (IV) curves with measured points per voltage of PV=11, 21, 51 and 101 measured pointsV. These two cycles are compared with two different common parameter extraction methods. The results given in this Part 1 are used in Part 2 to calculate the five solar cell parameters of IV curves found in the literature.

Junction Engineering-Based Modeling and Optimization of Deep Junction Silicon Single-Photon Avalanche Diodes for Device Scaling

A silicon single-photon avalanche diode (Si-SPAD) with a deep multiplication zone was fabricated using 0.11- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> -CMOS technology. Deep n-well (DNW) implantation with three energy levels was performed to prepare a deep junction, and the highest breakdown voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\text {BR}}$ </tex-math></inline-formula> ) and lowest dark count rate (DCR) were obtained at the highest DNW implantation energy. In addition, devices with different structures were manufactured: a shallow junction, deep junction with mask DNW, deep junction with mask DNW without shallow trench isolation (STI), and deep junction with blank DNW. The blank DNW structure exhibited a lower edge breakdown risk and less deviation between samples compared to those of mask DNW structure in terms of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\text {BR}}$ </tex-math></inline-formula> and DCR. Moreover, a new edge breakdown risk was observed between DNW and p-well (PW) in the DNW blank structure, which could be alleviated by adjusting the PW sizes. According to the measurement results, the STI structure was preferable in terms of fill factor. Measurements were obtained from real fabricated test device structures, and technology computer-aided design (TCAD) for various factors associated with Si-SPADs with deep multiplication structures was performed.

Didactic model of the diode with Finite Element Method

The union of two extrinsic semiconductors, one of them doped with donor impurities and the other with acceptor impurities, constitutes what is called the diode. In this paper we present a didactic model of diode, from which its electrical characteristics can be extracted, such as the structure of energy bands, the characteristic curve of current voltage, etc. To achieve this purpose the Finite Element Method (FEM) was used. In essence, the MEF is a method that allows numerically approximate the solution of partial differential equations. It is well known that, except in very particular cases, obtain analytical solutions of such equations is impossible, and the only possibility is to find approximate solutions. This model is implemented in the search for new strategies and methods of teaching concepts related to electronic devices, such as diodes, transistors, light-emitting diodes, quantum devices, etc.

Modeling and Optimization of Triple Diode Model of Dye-Sensitized Solar Panel Using Heterogeneous Marine Predators Algorithm

The reliable mathematical model construction of dye-sensitized solar cells (DSSCs) using the triple-diode model (TDM) is proposed but it is a challenge due to its complexity. This work implements a novel method incorporating a recent meta-heuristic optimizer called the heterogeneous marine predators algorithm (H-MPA) to identify the nine parameters of the triple-diode equivalent circuit of DSSCs. In the optimization procedure, the nine unknown parameters of TDM are employed as decision variables, but the objective function to be minimized is the root mean square error (RMSE) between the experimental data and the estimated data. To prove the superiority of the H-MPA, the obtained results are compared with the slime mold algorithm (SMA), Transient search optimizer (TSO), Manta-Ray Foraging Optimization algorithm (MRFO), Forensic-Based Investigation (FBI), Equilibrium optimizer (EO), and Artificial ecosystem-based optimizer. The primary findings demonstrated the superiority of the proposed strategy in building a consistent model of the triple-diode model of DSSCs.

Green electricity and medical electrolytic oxygen from solar energy - A sustainable solution for rural hospitals

The objective of this paper is to design and simulate for rural areas isolated from the electricity grid, a system based on solar energy for the optimal supply of green electricity and medical oxygen to a hospital. The system sized to produce 20 Nm3/day is constituted of a 37.46 kW photovoltaic farm, a 15.47 kW electrolyzer, and a 15.47 kW fuel cell. The simulation of the Photovoltaic system is performed using the single diode model solved with the Lambert function defined in MATLAB Software. The daily production of oxygen and hydrogen during the sunniest day of the month is respectively 20.81 Nm3/day and 41.61 Nm3/day. The daily energy that can be stored is relevant to the hydrogen production and an electricity storage capacity of 124.89 kWh is feasible. During the least sunny day of the least sunny month, the daily production of oxygen and hydrogen is respectively, 7.72 Nm3/day and 15.44 Nm3/day. The recorded values prove that the system sized can constitute a viable solution to ensure the permanent supply a green electricity and oxygen to the hospital with good energy storage capacity.

Comparative Analysis of Different Iterative Methods for Solving Current–Voltage Characteristics of Double and Triple Diode Models of Solar Cells

The current–voltage characteristics of the double diode and triple diode models of solar cells are highly nonlinear functions, for which there is no analytical solution. Hence, an iterative approach for calculating the current as a function of voltage is required to estimate the parameters of these models, regardless of the approach (metaheuristic, hybrid, etc.) used. In this regard, this paper investigates the performance of four standard iterative methods (Newton, modified Newton, Secant, and Regula Falsi) and one advanced iterative method based on the Lambert W function. The comparison was performed in terms of the required number of iterations for calculating the current as a function of voltage with reasonable accuracy. Impact of the initial conditions on these methods’ performance and the time consumed was also investigated. Tests were performed for different parameters of the well-known RTC France solar cell and Photowatt-PWP module used in many research works for the triple and double diode models. The advanced iterative method based on the Lambert W function is almost independent of the initial conditions and more efficient and precise than the other iterative methods investigated in this work.

Hybrid modeling of perovskite light-emitting diodes with nanostructured emissive layers.

Perovskite light-emitting diodes (PeLEDs) have attracted much attention due to their superior performance. When a bottleneck of energy conversion efficiency is achieved with materials engineering, nanostructure incorporation proves to be a feasible approach to further improve device efficiencies via light extraction enhancement. The finite-difference time-domain simulation is widely used for optical analysis of nanostructured optoelectronic devices, but reliable modeling of PeLEDs with nanostructured emissive layers remains unmet due to the difficulty of locating dipole light sources. Herein we established a hybrid process for modeling light emission behaviors of such nanostructured PeLEDs by calibrating light source distribution through electrical simulations. This hybrid modeling method serves as a universal tool for structure optimization of light-emitting diodes with nanostructured emissive layers.

Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

AbstractMeasurement data based on current and voltage of photovoltaic (PV) systems and the establishment of more accurate and stable solar system models are of typical significance for the design, control, evaluation and optimization of PV systems. Accurate and stable parameter evaluation for PV systems needs to be based on more efficient optimization techniques to achieve efficient energy conversion from solar energy. Therefore, this paper proposes a novel and efficient optimization technique enhanced colony predation algorithm to solve the complex PV parameter identification problem named ECPA. By fusing extremal optimization strategy and Nelder–Mead simplex method enables ECPA to further develop in the neighborhood of potential optimal solutions while improving the position of inferior agent candidates, and finally has the ability to search globally beyond the local optimum. To verify the optimization efficiency of ECPA, the first part verifies the efficiency of ECPA in solving complex high‐dimensional and multimodal problems by conducting competitive comparison experiments at the IEEE CEC 2020 benchmark case. In the second part, ECPA is compared with nine similar published state‐of‐the‐art algorithms, and competitive tests for PV parameter identification under single diode model, double diode model, triple diode model and PV module model (PV) are conducted. Finally, we focused on three different commercial PV models (thin film ST40, monocrystalline SM55, and multicrystalline KC200GT) to test the accuracy of ECPA in evaluating PV parameters. The test results show that ECPA is able to maintain a high level of accuracy and stability when dealing with commercial PV models in complex environments. The experimental results demonstrate that ECPA outperforms other algorithms in terms of data fitting, stability, convergence speed and convergence accuracy. All the competitive experimental results show that ECPA can be a novel technique with the best performance for identifying the parameters to be determined in solar PV systems.

Peafowl optimization algorithm based PV cell models parameter identification

Photovoltaic (PV) power generation can considerably reduce the consumption of traditional fossil energy and improve environmental problems. Reliable photovoltaic (PV) cell modelling owns great significance to the following output characteristics analysis and optimal operation of the whole PV system, while there are several unknown physical parameters within different PV cell models. Thus, the identification of the internal parameters of the PV cell model is the first and foremost step for PV cell modelling, nevertheless, the intrinsic highly complex and non-linear and multi-modal features make traditional approaches, such as analytical methods hard to achieve satisfactory performance in solving this problem. Hence, this work aims to employ a powerful tool to effectively and efficiently overcome this thorny problem based on the most advanced optimization method. A recently developed meta-heuristic algorithm called peafowl optimization algorithm (POA) is employed in this work for PV cell modelling parameter identification. For comprehensive validation, two different PV cell models, i.e., double diode model (DDM) and triple diode model (TDM) are utilized. Simulation results demonstrate that POA can more accurately identify the unknown parameters of PV cell models in a higher convergence speed compared against other algorithms.

Diode model of nonuniform irradiation treatment to predict multiscale solar-electrical conversion for the concentrating plasmonic photovoltaic system

Optical concentrators and plasmonic nanostructured surfaces are promising methods to increase the solar irradiation density and enhance light absorption. The combined application of these two methods for solar cells faces challenges of multiscale solar-electrical conversions, especially the treatment of nonuniform irradiation. A multiscale multiphysics method is proposed to predict the electrical outputs of a concentrating plasmonic photovoltaic (CPV) system. In the model, a series-connected diode global model for concentrating-plasmonic-electrical conversion combined with a local one-unit diode for fully coupled optical-thermal-electrical conversion is employed to treat nonuniform concentrating irradiation. The one-unit diode model is calibrated by J-V curve fitting with the local fully coupled optical-thermal-electrical model to characterize the wavelength-dependent light absorption and charge carrier behaviors. An explicit formula is proposed to consider the influences of concentrating irradiation and plasmonic nanostructures. The feasibility of the calibrated one-unit diode under concentration ratios is validated with the maximum power compared to an experimental GaAs-based solar cell under concentrated sunlight. The electrical outputs of the plasmonic solar cell under nonuniform irradiation is obtained by the equivalent circuit with series-connected diodes. Each of the diodes yields a photogenerated current in its individual state, whereas the equivalent series-connected circuit has a reformulated homogeneous current. The cross current of the series-connected circuit is mainly determined by the solar cell zone with the lowest concentration ratio. The method with a uniform one-sun irradiation assumption in the entire solar cell surface can overestimate its working current before the declining point and can underestimate the open-circuit voltage. The concentration enhances the photogenerated current but induces auxiliary series resistance. The maximum power is a trade-off of the improved photogenerated current and unfavorable series resistance. This work provides theoretical guidance for large-scale engineering applications of CPV systems with nanostructured solar cells.

A twofold hunting trip African vultures algorithm for the optimal extraction of photovoltaic generator model parameters

ABSTRACT The development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the African Vultures Optimization Algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models’ accuracies were notably improved with RMSE scores of 6.9096 × 10 − 4 and 7.4011 × 10 − 4 respectively for the RTC France cell, and 1.4251 × 10 − 2 for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices.

Improved honey badger algorithms for parameter extraction in photovoltaic models

Precise estimation of the parameter values of solar models is very essential for optimization of solar systems. Many studies that use metaheuristic algorithms have recently been proposed for parameter estimation and optimization in photovoltaic models. In this study, it is aimed to enhance convergence performance in photovoltaic systems by scanning the search space using two improved versions of the honey badger algorithm. First, the Gauss/Mouse map-based chaotic honey badger algorithm has been considered motivating by the fact that chaotic maps are successful in controlling critical random values in exploration and exploitation phases. The other algorithm is based on hybridization of opposition based learning with honey badger algorithm. Opposition based learning has efficient convergence capability as it scans the search space using opposites of candidate solutions. The performances of these improved methods and recent metaheuristic optimization algorithms are firstly evaluated for CEC2017 and CEC2019 datasets. After obtaining the successful results for these datasets, proposed algorithms are compared for single-diode, double diode and photovoltaic module models which are given as poly-crystalline Photowatt-PWP201, mono-crystalline STM6-40/36, and poly-crystalline STP6-120/36. For each model, optimum parameter values are found minimizing the root-mean-square-error. In addition, three commercial PV panels; Mono-crystaline SM55, Thin-film ST40, and Multi-crystalline KC200GT are also considered for performance evaluation. According to simulation results, proposed algorithm exhibits high performance in terms of minimization of root mean square error. The compliance of estimated and actual values of the parameters are visualized with current–voltage (I–V) and power–voltage (P–V) characteristics. The results show that the proposed methods are effective alternatives for solution of photovoltaic parameter estimation problem and contribute to the parameter optimization of photovoltaic models.

Physical modeling and simulation analysis of dynamic characteristics of pin power diode

The physical description of the dynamic characteristics of power electronic devices is particularly important for the analysis of the dynamic characteristics of power electronic devices based on the new power system. Firstly, the physical model of pin high-power diode is established to analyze the changes of current and voltage when the power diode is turned on and off. Then, the dynamic simulation model of the power diode is built based on Simulink. The characteristic of the model is that the resistance diode inductance circuit (RDL circuit) is used to simulate the transient current in the process of on and off, and the physical process is explained. The first mock exam of the universal ZP1000A type PIN power diode is carried out on this model platform, which proves the main performance of di/dt and dv/dt of power electronic devices and the validity of the proposed model.

Solar Photovoltaic Cell Parameter Identification Based on Improved Honey Badger Algorithm

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

Investigation of a social group assisted differential evolution for the optimal PV parameter extraction of standard and modified diode models

Parameter estimation of PV cells is essential to develop accurate simulation techniques, optimal control mechanisms, converter design optimization, maximize the efficiency of PV harvesting systems, improve the accuracy of monitoring systems etc. Advanced meta-heuristic algorithms for the optimal parameter estimation through the minimization of the disparity between the measured and estimated I-V data pairs have emerged as a powerful tool in this regard. Despite the efficacy of the meta-heuristics for the standard diode models, the optimization of modified diode models including the higher-order complex modified double and triple diode modules remains largely unexplored. Hence, an advanced hybrid meta-heuristic technique benefitting from the synergy of social group optimization and differential evolution known as social group assisted differential evolution (SGDE) is developed to achieve an enhanced balance of exploration and exploitation for the complex multi-modal landscapes of the modified diode models. Three experiments involving the minimization of the root mean square error for three commercial PV modules (KC200GT, Shell SQ85 and Shell ST40) based on the simulated I-V data followed by four experimental I-V curves of two solar cells (RTC France solar cell and PVM 752 GaAs thin-film cell) and two PV modules (Photowatt-PWP201 and STM6-40/36 PV modules) and the parameter optimization for two commercial modules (JAM72S01-380/PR and SUNCECO SEP300W PV modules) at varying levels of irradiance and temperature are investigated and extensively compared with fourteen other state-of-the-art meta-heuristic techniques. SGDE outperforms the competitor algorithms in terms of optimality, consistency and demonstrated greater robustness to local entrapment for the standard and modified diode models.

Scalable p-i-n Diode Modeling and Parameter Extraction for Use in the Design of W-Band GaAs Switch

Scalable modeling and parameter extraction of the millimeter-wave GaAs-based p-i-n diode for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> -band switch design are presented in this article. A direct-extraction method based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> -parameters on-wafer measurement is utilized to determine the extrinsic and intrinsic model parameters without any de-embedding test structures. Under turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> bias conditions, the modeled input reflection coefficients agree well with the measured data in the entire frequency ranges for GaAs-based p-i-n diode. The scalable normalization rules have been used to design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> -band p-i-n diode switch successfully; good agreements are obtained between the simulated and measured data to verify the accuracy of the proposed model.

Design of a Broadband Switchable Active Frequency Selective Surfaces Based on Modified Diode Model

In this letter, a broadband switchable active frequency selective surfaces (AFSSs) is presented, and the proposed structure is comprised of three metallic layers and two Rogers RT5880 substrates. In order to realize broadband transmission, a second-order bandpass filter is constructed, and the multilayer coupling effect is utilized. A modified equivalent circuit model of the p-i-n diode is proposed to reduce the simulation error and improve the design accuracy. By controlling the bias voltage of the p-i-n diodes, two completely different functions are realized. At the function of dual-polarized shielding, the shielding bandwidth is 2–18 GHz, and the transmission bandwidth is 8.2–10.5 GHz with a fractional bandwidth of 24.6% at the function of dual-polarized transmission. A prototype has been fabricated and measured, and the measurement results are in good agreement with the simulation results, indicating that the AFSSs has good polarization stability for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band radar applications.

EXTRACTION OF DOUBLE-DIODE PHOTOVOLTAIC MODULE MODEL’S PARAMETERS USING HYBRID OPTIMIZATION ALGORITHM

This paper presents seven parameters of double diode model of the photovoltaic module under different weather conditions are extracted using differential development with an integrated mutation per iteration (DEIM) algorithm. The algorithm is produced by integrating of two other algorithms namely, electromagnetism like (EML) and differential evolution (DE) algorithms. DEIM enhances the mutation step of the original DE by using the attraction-repulsion principle found in the EML algorithm. Meanwhile, a novel strategy based on adjusting mutation and crossover rate factors for each iteration is adopted in this paper. The implemented scheme's success is confirmed by comparing its results with those obtained by techniques cited in the literature. Along with the results, the DEIM suggests high closeness with the experimental I–V characteristic. For the proposed algorithm an average Root Mean Square Error (RMSE), absolute error (AE), mean bias Error (MBE), and execution time values were 0.0608, 0.044, 0.0053 and 23.333, respectively. The comparisons and evaluations results proved that the DEIM is better in terms of precision and rapid convergence. Furthermore, fewer control parameters are needed in comparison to EML and DE algorithms.