Parameter Extraction Method Research Articles

An Optimal Parameter Extraction Procedure for SiC Power MOSFET Model

A simple and efficient parameter extraction method for Silicon Carbide (SiC) power MOSFET model is described. This method uses nonlinear optimization algorithm to find the optimal set of parameters to model. The optimizer algorithm starts with initial guess parameters, extracted from measurement, to provide a set of parameters minimizing errors between model and measurements data in entire operating regions of the device. The starting initial guess parameter values give to the algorithm a closed solution to obtain the optimal set of model parameters with reduced iteratives. The Levenberg-Marquardt (LM) algorithm will be used in this work. The efficiency of the proposed extraction method is proved with the good agreements obtained between the model and the measurements.

Signal drift modeling and analysis of an intelligent instrument using a multivariate adaptive regression splines technique

Signal drift is an important failure mode of intelligent instruments under the combined effect of complex loading conditions and usually exhibits strongly nonlinear characteristics with a high degree of scatter. To solve this problem, this paper applies a multivariate adaptive regression splines (MARS) model to flexibly fit the nonlinear and highly inconsistent signal drifts of intelligent instruments. To investigate the pressure signal drift characteristics of different test transmitters, a unified parameter extraction method is proposed that uses a four-loop recursive algorithm to obtain consistent MARS model parameters. Through an application case study on five intelligent pressure transmitters tested at two temperatures, it is verified that the proposed unified parameter extraction method works perfectly for the fitting and analysis of pressure signal drift of these transmitters. Furthermore, it has advantages in providing quantitative interpretation of signal drift scatter, with potential practical application to investigation and comparison of drift fault curves of instruments that exhibit significant inconsistency.

An analytical approach based on coupled multi-physics model for photovoltaic arrays performance simulation

Simulating the output performance of a proposed photovoltaic plant is a necessary phase prior to the construction stage and has been intensively studied for decades. The requirement for accurate and reliable simulation needs a comprehensive analysis of the photovoltaic power generator considering environmental factors and device behaviors. Unlike other published work, this paper presents an analytical approach to simulate the output performance of photovoltaic arrays using coupled multi-physics modeling without the support of historical data. The interplay among the electrical and thermal models, device characteristics, and device topologies are deeply investigated and formulated. In the electrical model, a parameter extraction method, as well as an iterative algorithm, are utilized on the equivalent circuit model for a quick solution of the I–V characteristics of the photovoltaic module. To obtain accurate results of the output power and its interaction with the environment, irradiation, temperature, humidity, wind speed, and wind direction are analyzed along with the photovoltaic output power in the thermal model. Meanwhile, this output power is constrained to the maximum power point tracking mechanism of the inverter. Real data from a photovoltaic plant are used for comprehensive analysis in both long-term and short-term scenarios. The experiment shows that the proposed approach is competitively accurate and feasible. Compared with the state-of-the-art studies, the proposed approach improves the simulation accuracy by 7.75%.

Improved Parameterization Methodology for a Field-Stop Trench-Gate IGBT Physical Model by Switching Feature Partitioning

Accurate simulation of IGBT characteristics is an essential task to predict its electrical behaviors. Numerous physics-based IGBT models have been developed and proven to produce satisfactory simulation results. However, unavailability of device parameters is the key roadblock to application of these physics-based models. Additionally, the coupling between device parameters renders parameter extraction very challenging and heavy. In this paper, a novel parameter extraction scheme by switching feature partitioning is proposed, which considerably relaxes the negative impact of parameter coupling during parameter extraction. The IGBT inductive turn-off process and static characteristics are divided into nine typical feature segments under an inductive load. Then, the physical principle of each feature segment is analyzed, and the corresponding influencing parameters of each feature segment are found. During the parameter extraction, the corresponding weights are assigned to the parameter search according to the error of each feature segment. The proposed method for parameter extraction is elaborated. Using the extracted parameters, the simulation results by the physics-based IGBT model are in excellent agreement with the experimental results of the tested IGBT, and the effectiveness of the proposed parameter extraction program is verified. Finally, the extracted parameters are evaluated and verified by global sensitivity analysis.

Spectral Relaxation Imaging Microscopy II: Complex Dynamics.

The dynamics of condensed matter can be measured by the time-dependent Stokes shift of a suitable fluorescent probe. The time-dependent spectral correlation function is typically described by one or more spectral relaxation correlation times, which, in liquid solvents, characterize the timescales of the dipolar relaxation processes around the excited-state probe. The phasor plot provides a powerful approach to represent and analyze time and frequency-domain data acquired as images, thus providing a spatial map of spectral dynamics in a complex structure such as a living cell. Measurements of the phase and modulation at two emission wavelength channels were shown to be sufficient to extract a single excited-state lifetime and a single spectral relaxation correlation time, supplying estimates of the mean rate of excited-state depopulation and the mean rate of spectral shift. In the present contribution, two more issues were addressed. First, the provision of analytic formulae allowing extraction of the initial generalized polarization and the relaxed generalized polarization, which characterize the fluorescence spectrum of the unrelaxed state and the fully relaxed state. Second, improved methods of model discrimination and model parameter extraction for more complex spectral relaxation phenomena. The analysis workflow was illustrated with examples from the literature.

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%.

Innovative approaches to extract double-diode model physical parameters of a PV module serving outdoors under real-world conditions

This paper presents innovative techniques for modeling and simulating photovoltaic (PV) solar modules. The major objective is to determine the seven parameters of Double-Diode Model (DDM) for PV modules serving indoors under standard test conditions (STC), non-STC conditions, or outdoors under real-world conditions. The first method employs a precise iterative technique utilizing numerical variation of two diode ideality factors on a newly derived transcendental equation relating series resistance to ideality factors and PV measurements to find values of series resistance, parallel conductance, photocurrent and two diode saturation currents at STC. The second method utilizes a system of non-linear equations corresponding to key-points of current–voltage (I-V) curve and numerical values of seven parameters at STC to determine time dependent values of DDM physical parameters throughout an arbitrary day under real-world conditions. The third is a predictive approach based on analytical formulas portraying effects of solar irradiance and temperature on DDM physical parameters for PV modules installed at any location and serving during an arbitrary day. The proposed techniques are validated with actual data of four PV modules of polycrystalline silicon (poly-Si) and monocrystalline silicon (mono-Si) technologies from National Renewable Energy Laboratory (NREL) database. Absolute Error (AE) values for DDM parameter extraction method at STC do not exceed 5 μA. Normalized Root Mean Square Error (NRMSE) ceiling value is equal to 3 % for both numerical approach of extraction and analytical approach of prediction under real-word conditions. The results demonstrate the reliability of extracted DDM physical parameters and their compatibility with experimental I-V data, lending credibility to these methods for analyzing and optimizing PV systems.

Image-based analysis of single jersey loop parameters

Quality Control is crucial when it comes to fine and technical knits. Digital Twins are common in product design and are necessary for heat and mass flow simulations of textile fabrics. In both cases primarily, the exact geometry of the knitted loop is necessary for following processes. Wale spacing W and course spacing C are the essential geometrical characteristics of the representative unit cell of single jersey fabrics. The loop parameters can be obtained manually by counting loops per unit length or by microscopic measurements, which both are time consuming and are not applicable in integrated digital processes. Therefore, a new method for automatic extraction of major loop geometry parameters is described. A state-of-the-art method, which analyzes the microscopic images automatically, works with Fourier-Transformation of the image brightness and is used as a benchmark. The new loop-based method extracts the geometrical data from the distances between the center points of the loops. The methods are evaluated on single jersey fabrics of three different yarns. In addition, the impact of magnification on the measurement is shown for one fabric. The new method gives same results as manual measurements and even more precise results as the benchmark method.

Numerical modeling of FS-trench IGBTs by TCAD and its parameter extraction method

The electrical behavior of the IGBTs is the key factors to the application of IGBTs. Technology Computer Aided Design (TCAD) is one of the most effective methods for characterizing of electrical behavior of IGBT. However, the great difficulties in extracting the model's parameters hinder its widely applications. In the present work, a high-precision TCAD model for FS-Trench IGBT was established and verified by experiments. Firstly, the parameters necessary for TCAD models were analyzed in theoretical and experimental. The key process parameters need to be determined in a value range table. A Convolutional Neural Network (CNN) was proposed and trained to form a relationship from characteristic curves to key process parameters. Subsequently, the numerical model's parameters were established by combining the trained CNN model with experimental data. Finally, the TCAD model was established and the simulations of IGBTs were carried out and verified by experiments. Results demonstrate that the proposed TCAD model is well consistent with experiments with a Root Mean Square Error (RMSE) of about 3.7 %. This work may provide a feasible method to obtain highly precise numerical models of power semiconductor devices. It may also promote the application of Artificial Intelligence in the device modeling and optimization design.

Direct parameter extraction method of EEHEMT nonlinear model for InP HEMT

AbstractA direct parameter extraction method of EEHEMT nonlinear empirical model is proposed in this paper. An improved DC model is also presented by considering the channel length modulation parameter () as a function of gate‐to‐source voltage. Model verification is carried out by comparison of measured and simulated current–voltage characteristics and S‐parameters. Good agreement is obtained between the measured and modeled results for 2 × 15 μm gatewidth (number of gate fingers × gatewidth) 70‐nm gatelength InP high electron mobility transistors (HEMT). An increased accuracy in modeling the I–V and RF characteristics is also obtained in the proposed model, compared with the conventional one.

Coupling covariance matrix adaptation with continuum modeling for determination of kinetic parameters associated with electrochemical CO2 reduction

Coupling covariance matrix adaptation with continuum modeling for determination of kinetic parameters associated with electrochemical CO2 reduction

Averaged Behavior Model of Current-Mode Buck Converters for Transient Power Noise Analysis

Accurate evaluation and simulation of power noise is critical in the development of modern electronic devices. However, the widely used target impedance fails to predict the low-frequency noise generated in a device due to the existence of the dc–dc converter, whose output impedance can change under different loading conditions. A physical circuit model is then desired to replicate the behavior of a voltage regulator module, and the average technique is an efficient method to estimate the noise of a pulsewidth-modulated (PWM) converter. With the emergence of converters with adaptive on-time (AOT) controllers, more complex averaging methods are required, but none of them supports transient simulation. A general, efficient, and accurate modeling technique is presented in this article, whose framework supports both current-mode PWM and AOT controllers. In addition, a novel two-step parameter extraction method is proposed, which can be used to evaluate the equivalent values of internal feedback parameters of an encrypted simulation model or from measurement. The modeling method is validated by both simulation and measurement.

Efficient Statistical Parameter Extraction for Modeling MOSFET Mismatch

In this paper, we propose an efficient statistical parameter extraction method to accurately model the random device mismatch of MOSFETs. The key idea is to approximate the performance variations as mathematical functions of device mismatch. Based on these approximated functions and the electrical test data, we solve the unknown statistical parameters by nonlinear optimization. Our numerical experiments demonstrate that the proposed method can remarkably improve the modeling accuracy with affordable computational cost, compared against the state-of-the-art techniques.

Compact Model Analysis for Low Voltage OFETs with Electrolytic Gate Dielectrics: Toward a Universal Model for Poly(3-Hexylthiophene) P3HT OFETs

The lightweight with flexibility and low-cost processing engineered the rapid growth of organic field-effect transistors (OFET) in the past three decades. Suitable compact models and parameter extraction methods are being developed to further the use of OFETs in integrated circuits, where stimulations are required to optimize the device performance. To simplify the parameter extraction, metaheuristic approaches are usually made, which otherwise is a cumbersome process. Following these, here investigations are made with the help of such a compact model to extract the operational parameters of P3HT (poly (3-hexylthiophene) based OFETs with electrolytic gate dielectrics using the genetic algorithm (GA) method. The result show that the compact model that was essentially developed in line with the successful models for inorganic material based FETs, can be used as an excellent framework for simulating low voltage OFETs made with both low and high mobility organic semiconductors. Mobility and threshold voltage calculated from the extracted parameters using GA for the two devices having mobility value differences of more than four orders are found to be nicely fitting with the experimental values. These results assume significance to the organic electronic industry as this facilitates the real-time circuit application of OFETs. KEYWORDS: Modeling, Low voltage OFET, Genetic algorithm, Ionic liquid, P3HT

Periodical evaluation of photovoltaic modules and diode parameter extraction method using multiple linear regression models

The stability and performance of photovoltaic (PV) modules can be assessed by outdoor testing where external conditions such as illumination and module temperature are measured at regular time intervals along with the jV-curve of the module. However, the fluctuation and seasonal variation of external conditions can make it difficult to trace changes such as degradation in PV-module properties (at e.g. standard test conditions). This contribution demonstrates the use of multiple linear regressions (MLR) to overcome these difficulties. The data gathered over large periods is condensed into a set of few predictors, that reproduce the jV parameters at infrequently encountered conditions that are required for comparison. Furthermore, the parameters of a physical device model are calculated directly from MLR-predictors, validating our procedure two-fold, by applying the MLR-method to simulated data, replicating the original input parameters, and comparing monthly parameter averages between the MLR-method and a known parameter extraction method.

A gait signal acquisition and parameter characterization method based on foot pressure detection combined with Azure Kinect system

The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.

Improvement of Treetop Displacement Detection by UAV-LiDAR Point Cloud Normalization: A Novel Method and A Case Study

Normalized point clouds (NPCs) derived from unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have been applied to extract relevant forest inventory information. However, detecting treetops from topographically normalized LiDAR points is challenging if the trees are located in steep terrain areas. In this study, a novel point cloud normalization method based on the imitated terrain (NPCIT) method was proposed to reduce the effect of vegetation point cloud normalization on crown deformation in regions with high slope gradients, and the ability of the treetop detection displacement model to quantify treetop displacements and tree height changes was improved, although the model did not consider the crown shape or angle. A forest farm in the mountainous region of south-central China was used as the study area, and the sample data showed that the detected treetop displacement increased rapidly in steep areas. With this work, we made an important contribution to theoretical analyses using the treetop detection displacement model with UAV-LiDAR NPCs at three levels: the method, model, and example levels. Our findings contribute to the development of more accurate treetop position identification and tree height parameter extraction methods involving LiDAR data.

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.

Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models

In today's technology, solar energy continues to be one of the leading renewable energy sources in electricity production methods due to its abundance on the Earth's surface and its non-polluting nature. In solar photovoltaic (PV) systems, it is relatively easy to produce electricity from the sun, and their efficiency can be enhanced by accurately estimating the electrical parameters of solar cells. From this point of view, this paper has focused on solar cell design to improve the performance of solar PV systems. To achieve this target, fitness-distance balance-based stochastic fractal search (FDB-SFS), particle swarm optimization (PSO), student psychology-based optimization (SPBO), and adaptive guided differential evolution (AGDE) algorithms are employed. The experimental analysis is performed on single-diode solar cell, double-diode solar cell, and three PV modules. Considering the experimental results, it is observed that the best estimation accuracy is reached by the FDB-SFS algorithm. Accordingly, root mean square error (RMSE) values between measured and estimated data were calculated to be 9.86E-04 for the single-diode solar cell model, 9.84E-04 for the double-diode solar cell model, 2.42E-03 for the Photowatt-PWP201 module, 1.72E-03 for STM6–40/36 module, and 1.67E-02 for STP6–120/36 module. Consequently, the present paper reports that FDB-SFS is an efficient and powerful method for parameter extraction of solar photovoltaic models.

Multi-component induction logging detection characteristics of the fracture azimuth and dip angles and the fast extraction methods of fracture distribution parameters

In the field of log interpretation of tight sandstone and shale reservoirs, it is difficult to quantitatively evaluate the fracture distribution parameters (fracture azimuth angle and fracture dip angle) near the borehole. Multi-component induction logging can measure the apparent resistivities of the reservoir in different directions by an orthogonal coil system, which lays a foundation for the quantitative evaluation of the fracture distribution parameters near the borehole. First, we derive the finite element equations for numerical calculation from the Maxwell equations for the time-harmonic field and construct a three-dimensional (3D) vector finite element numerical algorithm for the fractured formation. Second, we quantitatively analyze the multi-component induction logging detection characteristics of the fracture azimuth and dip angles. Third, we establish a fast extraction method of the fracture azimuth angle based on coordinate system rotation and an analytical calculation method of the fracture dip angle using the combination of cross components and coplanar components of multi-component induction logging. Finally, we use field data to test the fracture distribution parameter extraction method, and the fracture dip and azimuth angles calculated by multi-component induction logging are consistent with those calculated by borehole imaging logging, which realizes the fast extraction of the distribution parameters of fractures in tight sandstone and shale reservoirs.