Parameter Extraction Technique Research Articles

Indoor PV Modeling Based on the One-Diode Model

The use of photovoltaic (PV) panels in interior spaces is expected to increase due to the proliferation of low-power sensor devices in the IoT domain. PV models are critical for estimating the I–V curves that define their performance at various light intensities. These models and the extraction of their parameters have been extensively studied under outdoor conditions, but their indoor illumination performance is less studied. With respect to the latter, several studies have used the parameter-scaling technique. However, the model’s accuracy degrades when the light level decreases. In this study, we propose a simple PV modeling technique that can be applied at various illuminance levels by only using characteristic points (short-circuit current, open-circuit voltage, and maximum-power voltage points) at a reference illumination level. The model uses the characteristic point translation technique to translate the reference characteristic points to other operating conditions. Then, parameter extraction technique is used to extract the model’s parameters. The proposed model’s accuracy is verified using two commercial PV panels and different indoor lighting technologies. The results indicate that the proposed model outperforms the other examined works in terms of accuracy, with an average improvement of 15.75%.

Analysis of Topographic Feature Parameters of Dinosaur Valley Ring Tectonic Geomorphology Based on the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM)

Geomorphological surveys and terrain analysis are essential for geomorphology, hydrology, and geographic information systems (GIS). Terrain characterization parameters are fundamental for comprehending geomorphological processes, delineating landforms, and evaluating geohazards. Hence, this study relies on data from the Gaofen-2 satellite (GF-2) and the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM). Focusing on the ring tectonics landform of Dinosaur Valley, the research utilizes visual interpretation, GIS terrain parameter extraction techniques, and visual mapping to identify, measure, and analyze terrain features. The results indicate that ALOS DEM can offer accurate geomorphic feature information. The optimal threshold for extracting the surface water system network is 150, and the optimal analysis window for extracting terrain feature parameters is 16 × 16 (0.04 km2), determined through statistical methods. This study reveals distinct double-ring linear features and prominent ring-shaped terrain characteristics by extracting terrain feature parameters and utilizing mathematical and statistical analysis. The topography gradually ascends from the center to the ridgeline and then descends to the valley line, resembling a bowl shape. This study highlights the potential application of ALOS DEM for accurately identifying topographic features in the intricate mountains of the Yunnan Plateau. It establishes a practical research framework for high-precision topographic datasets and geomorphological characterization, facilitating further analysis of the evolution of topographic development and the assessment of tectonic activity.

Application of RSAPSO Hybrid Optimizer for Parameter Extraction of Solar PV Cell Models under Temperature Variations

Accurate modeling of photovoltaic (PV) modules/cells is crucial for evaluating the efficiency of solar PV systems. However, the lack of specific parameters of solar cells, which are not included in the manufacturer’s datasheet, often results in flawed cell modeling. Quick and convenient parameter extraction techniques are required to overcome this challenge and create a robust solar PV cell model. These models are useful in optimization, simulation, and enhanced energy harvesting from PV-based renewable energy systems. This paper uses a recent optimizer called Rat Search Algorithm Particle Swarm Optimization (RSAPSO), for extracting the parameters of single diode and double diode models of PV cells. RSAPSO combines the exploration and exploitation advantages of both algorithms. The RSAPSO's parameter optimization results were compared against five other techniques, and the superiority of the suggested algorithm was confirmed through ranking tests, statistical error analyses, and temperature variation sensitivity analyses.

Research on parameter extraction of thin‐film transistors based on swarm intelligence

AbstractThe development of integrated circuits for displays and other applications requires semiconductor device models and appropriate parameter extraction techniques to predict and understand the circuit behavior. These techniques are paramount in reducing design errors and shortening the product development cycle. This paper presents an algorithm that employed swarm intelligence in exploring an automated and accurate parameter extraction technology. First, an automatic parameter extraction of Rensselaer Polytechnic Institute (RPI) Model for polysilicon thin‐film transistor (Poly‐Si TFT) is achieved by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Compared with the best solution of the GA algorithm for automatic parameter extraction, the PSO outperformed the GA. However, it still prematurely converges to the suboptimal solution henceforth cannot obtain the expected solution accuracy. Second, the mutual learning particle swarm optimization (MLPSO) algorithm is proposed that introduces the concept of “mutual learning.” The new algorithm aims to find the global optimum in getting suitable trade‐off between exploration and exploitation. In addition, the MLPSO algorithm implemented the novel random initialization and fitness function in simplifying the complex manual processes and the empirical calibration, and it led to achieving automatic and accurate parameters extraction.

A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules

In this work, a multi-objective Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA) parameter extraction technique for photovoltaic (PV) modules is discussed. First, the efficacy of the Hybrid BESSA was proved via testing on unimodal functions, multimodal functions, and fixed dimensional multimodal functions and the results were compared with the Bald Eagle Search (BES) and other recently proposed optimization techniques. Then, a multi-objective Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA) parameter extraction technique was devised for photovoltaic (PV) module parameter extraction. The Hybrid BESSA parameter extraction technique was simulated and analyzed in the MATLAB/SIMULINK environment and in a practical experimental setup for the PV Module AS-M3607-S (G1 CELLS). It was found that the Hybrid BESSA possessed better exploration and exploitation capabilities as compared to the BES and other state-of-the-art techniques. It was found that the fitness function value derived by the Hybrid BESSA technique was less than that of the BES technique when tested under various weather conditions. The percentage error for open circuit voltage, output power, and short circuit current was lower when derived by the Hybrid BESSA in comparison with the BES technique. From the results obtained from modeling the PV Module AS-M3607-S (G1 CELLS) based on Hybrid BESSA-based extracted parameters and BES-based extracted parameters, it was seen that percentage improvement in the combined objective function for the condition of keeping irradiance fixed at 1000 W/m2 at a temperature varying from −30 °C, 0 °C, 25 °C, 30 °C, 50 °C, and 70 °C were 0.9%, 8.5%, 29.2%, 0.03%, 5.7%, and 0.5%, respectively. When the temperature was kept fixed at 250 °C and irradiance varied from 1000 W/m2, 800 W/m2, 600 W/m2, and 400 W/m2, the percentage improvement in combined objective function was found to be 0.5%, 8.1%, 0.5%, and 0.8%, respectively. By analyzing the simulation as well as the experimental results, it was established that the PV model parameter extraction method based on the Hybrid BESSA is more accurate than the BES technique. This analysis is based on a single-diode PV module. A double-diode PV module analysis still needs to be explored.

Accurate SiC MOSFET Chip Extraction Based on Parasitic Parameter Impact Compensation

With the growing use of SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> in power electronic equipment, it is meaningful to extract precise typical waveforms and characteristics for device manufacture, converter design, and operating evaluation. Many solutions have been proposed to improve measurement precision by equipment and essential techniques. However, the measured object should also be accurately identified as the existing measurement result consisting of the expected gate-source voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</sub> ) and voltage drops on parasitic parameters between measuring points. This article, first, reveals the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</sub> measurement deviation of TO-247-4 and TO-247-3 devices during the switching process and crosstalk, based on the equivalent circuit model and simulation waveforms. According to the clarification of the measured object, an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</sub> extraction method is proposed to eliminate this deviation. Voltage drops on parasitic parameters are compensated, with the help of high-precision equipment and the proven parasitic parameter extraction technique. Experiments are constructed to verify the analysis of measurement deviation and the availability of the proposed method. Comparison between various operating conditions emphasizes its necessity, especially in high-capacity and low-driving-resistor applications, contributing to characterization, operating condition monitoring, protection design, and measurement understanding of wide band-gap devices.

Pair-Reaction Dynamics in Water: Competition of Memory, Potential Shape, and Inertial Effects.

When described by a one-dimensional reaction coordinate, pair-reaction rates in a solvent depend, in addition to the potential barrier height and the friction coefficient, on the potential shape, the effective mass, and the friction relaxation spectrum, but a rate theory that accurately accounts for all of these effects does not exist. After a review of classical reaction-rate theories, we show how to extract all parameters of the generalized Langevin equation (GLE) and, in particular, the friction memory function from molecular dynamics (MD) simulations of two prototypical pair reactions in water, the dissociation of NaCl and of two methane molecules. The memory exhibits multiple time scales and, for NaCl, pronounced oscillatory components. Simulations of the GLE by Markovian embedding techniques accurately reproduce the pair-reaction kinetics from MD simulations without any fitting parameters, which confirms the accuracy of the approximative form of the GLE and of the parameter extraction techniques. By modification of the GLE parameters, we investigate the relative importance of memory, mass, and potential shape effects. Neglect of memory slows down NaCl and methane dissociation by roughly a factor of 2; neglect of mass accelerates reactions by a similar factor, and the harmonic approximation of the potential shape gives rise to slight acceleration. This partial error cancellation explains why Kramers' theory, which neglects memory effects and treats the potential shape in harmonic approximation, describes reaction rates better than more sophisticated theories. In essence, all three effects, friction memory, inertia, and the potential shape nonharmonicity, are important to quantitatively describe pair-reaction kinetics in water.

Prediction and Measurement Techniques for Radiated EMI of Power Converters with Cables

Recently, radiated electromagnetic interference (EMI) has become a research hotspot in power electronics systems, as the switching frequencies of power electronics systems have increased significantly with the adoption of wide-bandgap devices. In this article, a generalized radiated EMI model for power electronics converters with power cables is first reviewed. The radiated EMI model is then developed for a flyback power converter with critical ground impedance included. Based on the developed model, accurate high-frequency parameter extraction techniques and a radiated EMI prediction technique are developed and experimentally validated. Finally, essential measurement techniques are identified and developed to accurately extract parameters for accurate EMI prediction. The effects of the resolution bandwidth of the spectrum analyzer and critical PCB ground impedance on the radiated EMI are experimentally validated. PCB's impact on the common-mode (CM) choke's impedance and the radiated EMI is further validated. Techniques for minimizing the undesired near-field couplings in parameter extraction are discussed. The predicted EMI properly agreed with the measured EMI in the range of 30–230 MHz based on the EN55032 3 m class B standard.

Parameter Extraction Technique for Evaluation of Inductive and Capacitive Elements of Three-Winding Coupled Inductor

Wide-bandgap devices have enabled miniaturization of magnetic components to enhance the power density of the converter. However, parasitic components become dominant at higher switching frequencies that actuate unwanted electromagnetic interference issues. Therefore, it is critical to model and evaluate them in compact magnetic structures. Since multiwinding coupled inductive structures are essential part of multiphase converters, this article introduces a measurement method to compute the parasitic elements of a three-winding coupled inductor. Results of the proposed method are verified by measuring the high-frequency emissions of the high-step-up converter with two inductor structures. Both inductors had similar inductances but different physical dimensions. With application of the proposed measurement methodology, parasitic capacitances of both inductors were computed. Results showed that one inductor structure has higher parasitic capacitance than the other. Since high-frequency emissions of the converter directly depend on the parasitic capacitance of the inductor structure, the emission profile of the converter showed that the converter indeed had lower noise peaks when the inductor with lower capacitance was employed. Hence, these results validate the capacitance trend as predicted by the proposed modeling technique. This method can be employed to any multiwinding coupled inductor to compute its parasitics to optimize the required electromagnetic interference filters.

Estimation of Photovoltaic Cell Parameters using the Honey Badger Algorithm

Optimal estimation of the intrinsic parameters of photovoltaic cells requires the use of meta-heuristics to increase their efficiency. This paper highlights the estimation of unknown parameters of a PV cell and module. For this purpose, the meta-heuristic optimization algorithm based on the Honey Badger Algorithm (HBA) principle is used. The simulation results via MATLAB prove that this algorithm has a good convergence. Indeed, the root mean square error (RMSE) is 9.8602×10-4, 9.8602×10-4, 2.4251×10-3, 1.7298×10-3 and 1.6783×10-2 for the single diode, dual diode, Photowatt-PWP201, Schutten Solar STM6-40/36 and the STP6-120/36 module respectively. Furthermore, the curves representing the current-voltage and power-voltage characteristics of the calculated unknown parameters versus those of the practical data measured from a PV cell/module datasheet coincide. The proposed algorithm can therefore be classified in the literature as one of the optimal parameter extraction techniques.

Micromechanical Model for Predicting the Tensile Properties of Guadua angustifolia Fibers Polypropylene-Based Composites.

In this paper, the one-dimensional tensile behavior of Guadua angustifolia Kunth fibre/polypropylene (PP+GAKS) composites is modeled. The classical model of Kelly–Tyson and its Bowyer–Bader’s solution is not able to reproduce the entire stress–strain curve of the composite. An integral (In-Built) micromechanical model proposed by Isitman and Aykol, initially for synthetic fiber-reinforced composites, was applied to predict micromechanical parameters in short natural fiber composites. The proposed method integrates both the information of the experimental stress-strain curves and the morphology of the fiber bundles within the composite to estimate the interfacial shear strength (IFSS), fiber orientation efficiency factor , fiber length efficiency factor and critical fiber length . It was possible to reproduce the stress-strain curves of the PP+GAKS composite with low residual standard deviation. A methodology was applied using X-ray microtomography and digital image processing techniques for the precise extraction of the micromechanical parameters involved in the model. The results showed good agreement with the experimental data.

Parameter identification of solar photovoltaic cell and module models via supply demand optimizer

The extraction of photovoltaic (PV) module parameters is regarded as a critical topic for assessing the performance of PV energy systems. The Supply-Demand-Based Optimization Algorithm (SDOA) is employed in this work to extract the unknown parameters of PV models. The SDOA mimics the stability and instability modes between the supply and the demand one in order that the quantity and price of commodities converge to the equilibrium point after a specified number of repetitions. It is frequently used to handle complicated nonlinear problems because of its ease of implementation and powerful optimization capabilities. The Triple-Diode Model (TDM) is extensively adopted in PV module mathematical models. The optimal nine TDM parameters are determined for the PVM 752GaAs PV thin film cell, whereas other solar irradiation and temperature values are used for the SQ 150 and MSX 60 modules. When used on the TDM model, the SDOA was used to verify the fitness values and standard deviation errors. Furthermore, the obtained result achieved by SDOA are contrasted with new techniques established in 2020 which are Backtracking Search Algorithm (BSA), Grey Wolf Optimizer (GWO), Bernstein-Levy Search Differential Evolution Algorithm (BSDE), Crow search Optimizer (CSO), and Manta Ray Foraging Optimizer (MRFO). For accurate and consistent results, thirty runs of the algorithm are executed for the modules and the standard deviations of fitness values are less than 1 × 10–18 for the triple diode model. In addition to this, a practical PV power plant system that lie in the Guizhou Power Grid of China is used to validate the efficiency of SDOA compared to other recent algorithms. Thus, SDOA is considered as a competitive optimizer among other reported techniques in the literature or recent techniques for PV parameter extraction.

An augmented small-signal model of InP HBT with its analytical-based parameter extraction technique

A rigorous and systematic analytical-based parameter extraction technique for the augmented small-signal equivalent-circuit model dedicated to III-V-based heterojunction bipolar transistors (HBTs) is presented. The proposed extraction method relies exclusively on S-parameters measured at low and high frequencies. The extraction algorithm is derived by peeling peripheral elements from the small-signal equivalent circuit with very few simplified approximations throughout the whole extraction process. All of the elements’ parameters of the small-signal equivalent-circuit model considering the base-collector and base-emitter capacitances distributed effect are determined accurately through the developed extraction technique. Excellent agreement is obtained demonstrating the comparisons between the small-signal S-parameters measured and modeled results for 0.7 μm InGaAs/InP HBTs over a wide range of bias points within the frequency range of 0.1–40 GHz, which verifies the validity and the accuracy of this model and extraction technique.

Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells

A precise, fast, and robust parameter extraction technique of solid oxide fuel cell models is extremely crucial for optimal control and behavior analysis. In this paper, a novel extreme learning machine based method is proposed to extract unknown parameters of solid oxide fuel cell models including electrochemical model and simple electrochemical model. At first, extreme learning machine is applied to overcome two thorny obstacles (e.g., data shortage and noised data) via predicting additional data and updating noised data. Then, both original data collected from a 5 kW solid oxide fuel cell stack and processed data are transferred to effectively guide eight prominent meta-heuristic algorithms for effective parameter extraction. The performance of extreme learning machine is thoroughly investigated in two typical operation conditions through a comprehensive comparison based on various training data. Simulation results validate that the proposed approach can effectively contribute to searching efficient model parameters along with high accuracy, prominent stability, high speed, and great robustness. Particularly, the accuracy of parameter extraction for electrochemical model and simple electrochemical model can be improved by 49.3% and 65.6% at most, respectively.

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).

An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models

The optimal operation of solar cells depends on the accurate determination of parameters in the Photovoltaic (PV) models, such as resistance and currents, which may vary due to unstable weathers conditions and equipment aging. The precise selection of these parameters resembles a multi-variable, nonlinear and multi-modal problem. Despite a few parameter extraction techniques being available to solve such a problem, more-accurate and advanced solutions still present a challenging research question. This paper therefore proposes an improved gaining-sharing knowledge (IGSK) algorithm to accurately and precisely extract the parameters of PV models. The improvement in the classical GSK algorithm is incorporated by introducing an adaptive mechanism to automatically adjust the value of the knowledge rate parameter. This adaptive mechanism ensures the balance between the number of dimensions updated by the junior gaining-sharing phase and the number of dimensions updated by the senior gaining-sharing phase. A bound-constraint handling method is also presented and a linear population size reduction technique is used to boost the speed of convergence and to maintain a tared-off between the exploration and exploitation properties. The efficacy of the proposed IGSK has been demonstrated by considering three different PV modules models, i.e., single diode, double diode, and PV modules and two other commercial ones (Thin Film ST40 and Mono-crystalline SM55). For those modules, the proposed IGSK receptively produces the following outcomes: 0.00098602188, 0.0009827277, 0.0024250749, 0.0017298137, and 0.016600603. The statistical obtained results demonstrate that the IGSK indicates competitive or even better performance on convergence speed, accuracy and reliability compared with other competing techniques. Therefore, the proposed approach is believed to be an effective and efficient alternative for parameter extraction of PV models.

Identification of power PIN diode design parameters: Circuit and device-based simulation approach

This paper aims to present a detailed systematic approach to identify the main design parameters of PIN power diodes. Firstly, the diode physical parameters are initialized using simple analytical equations. The second phase is the optimization of the diode parameters considering PSPICE circuit simulation where an electro-thermal physically based circuit model is utilized depending on a series of dynamic and static measurements. The final optimization step is carried out by using TCAD simulations. First, the diode extracted parameters are used to virtually fabricate the diode by using a process simulator. Then, using the output of the process simulator, a device simulator is used to get the desired output that is validated against experimental data. Three case studies for different power diodes are presented showing a good agreement between circuit/device simulation results and measurements. The presented methodology provides high accuracy like TCAD-based parameter extraction procedure with less time. In addition, it gives higher accuracy than the widely used circuit-based parameter extraction technique.

Improved small‐signal hybrid parameter‐extraction technique for AlGaN / GaN high electron mobility transistors

An improved hybrid parameter-extraction technique is proposed for the small-signal modeling application in AlGaN/GaN high electron mobility transistors (HEMTs). The capacitance partitioning characteristics of cold pinch-off GaN device with non-field-plate and field-plate structures are reinvestigated and the scanning range of parasitic capacitances Cpg and Cpd is determined. A systematic optimization approach is proposed to obtain the initial values of Cpg and Cpd. By using the global optimization algorithm, particle swarm optimization, the reliable parasitic parameters can be acquired based on the proposed error function. The validity of the developed hybrid parameter-extraction method is verified by 2 × 100 μm AlGaN/GaN HEMT with source-connected FP structure from 0.1 to 40 GHz among the 442 biasing points.

Extraction of Compact Static Thermal Model Parameters for SiGe HBTs

In this article, we present and evaluate compact static thermal model parameter extraction techniques for modern silicon germanium heterojunction bipolar transistors (SiGe HBTs). We found that the model implementation of thermal resistance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {th}}$ </tex-math></inline-formula> ) based on only junction temperature is implicit requiring time-consuming iterative procedure which may lead to potential instabilities. Dedicated extraction techniques are proposed for obtaining compact model-specific <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {th}}$ </tex-math></inline-formula> and its temperature coefficient. The proposed method is primarily validated on SPICE generated synthetic data. Next in order to showcase a compact model-independent verification, we also test the method using detailed thermal simulation from TCAD. Finally, we apply our extraction technique on measured data from fabricated transistors. The results are benchmarked to already obtained nominal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {th}}$ </tex-math></inline-formula> values from the same device family.

An Efficient Optimization Scheme for MIMO Antenna Decoupling Networks Using Space Mapping Techniques

The design of multiple input multiple output (MIMO) antennas using decoupling networks (DNs) often requires optimization and is computationally time consuming when using full electromagnetic solvers. To reduce the computational cost, the use of space mapping (SM) techniques in MIMO antenna design is proposed. Two approaches are considered for developing the necessary SM coarse model for MIMO antennas. The first is based on conventional DNs while the second uses a novel application of the internal multiport method (IMPM). Various parameter extraction (PE) techniques for SM techniques suitable for MIMO DN design are also developed. Two MIMO antennas are optimized to demonstrate the effectiveness and efficiency of the proposed schemes. Compared to conventional optimization, the proposed schemes improve the optimization speed by factors of approximately 20 and importantly, meeting the antenna design specifications for all examples.