Parameter Estimation Method Research Articles

Advancing UAV Sensor Fault Diagnosis based on Prior Knowledge and Graph Convolutional Network

Unmanned Aerial Vehicles (UAVs) are commonly equipped with multiple sensors to facilitate control and navigation. However, due to the complexity of the flight environment, the UAV sensors are prone to damage, which can cause serious accidents or even economic losses. Therefore, it is of great importance to propose an effective UAV sensor fault diagnosis method. The following weaknesses are obtained by the existing fault diagnosis methods: 1) The model-based methods, such as parameter estimation, state estimation, and equivalent space method, require introducing high-precision system models and expert knowledge. 2) Conventional graph convolutional network (GCN)-based methods fail to guarantee the accuracy of association graphs. To address the above two challenges, the dynamics model of quadrotor as prior knowledge is proposed in this paper to construct the association graph first. Then, a spatial-temporal difference graph convolutional network (STDGCN) consisting of a difference layer and multiple spatial-temporal graph convolutional modules is built. The difference layer enhances the feature extraction of the graph nodes, while the spatial-temporal graph convolutional modules extract the spatial-temporal dependencies of sensor data. Finally, the robustness and effectiveness of STDGCN are demonstrated in fault diagnosis compared with the commonly used data-driven methods. The experimental result shows that the proposed STDGCN method exhibits outstanding feature extraction ability with the highest diagnostic accuracy.

Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis

The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.

Fast constrained optimization for cloth simulation parameters from static drapes

AbstractWe present a cloth simulation parameter estimation method that integrates the flexibility of global optimization with the speed of neural networks. While global optimization allows for varied designs in objective functions and specifying the range of optimization variables, it requires thousands of objective function evaluations. Each evaluation, which involves a cloth simulation, is computationally demanding and impractical time‐wise. On the other hand, neural network learning methods offer quick estimation results but face challenges such as the need for data collection, re‐training when input data formats change, and difficulties in setting constraints on variable ranges. Our proposed method addresses these issues by replacing the simulation process, typically necessary for objective function evaluations in global optimization, with a neural network for inference. We demonstrate that, once an estimation model is trained, optimization for various objective functions becomes straightforward. Moreover, we illustrate that it is possible to achieve optimization results that reflect the intentions of expert users through visualization of a wide optimization space and the use of range constraints.

Comparative Study of Parameter Estimation Methods in Pharmacokinetic Model with Oral Administration: Simulations of Theophylline Drug Concentration

Comparative Study of Parameter Estimation Methods in Pharmacokinetic Model with Oral Administration: Simulations of Theophylline Drug Concentration

A Partial Maximum Likelihood Method to Estimate Cox Model for Competing Risk Data with Application

Many Research and clinical studies have addressed the occurrence of failure (death). As a result of other (external) factors, which may introduce additional risks that compete with the event under study. The resulting data refers to the complete data on Competing risks, which are affected by time differences. The exact times of occurrence of these risks are known, meaning the times of failure are observed for all observations with certainty. The impact of these risks on the hazard function is estimated based on the Cox proportional hazards model, which is estimated using the partial maximum likelihood method and numerical algorithms for parameter estimation. This includes the effect of variables on the Cox hazard function and the nonparametric part, estimated by assessing the effect of time on the hazard function using the Kaplan-Meier formula and calculating competing risks through the cumulative hazard function. These methods were applied to experimental data through large-scale simulations of different sizes and parameters and for several arithmetic means and standard deviations models. Moreover, applied to real data from a sample of 80 individuals with breast cancer. Analyzing the simulation results and real data revealed that the Downhill algorithm outperforms the Newton-Raphson algorithm in terms of estimation accuracy and efficiency, based on the statistical criterion for comparison, the root mean square error. In addition, competitive risks explained the effect of common variables in increasing competitive risks beyond the hazard function of the Cox model of the Newton-Raphson estimator. While it is converging to the hazard function of the Cox model for the Downhill estimator . Paper type: Research paper

Multi-interest adaptive unscented Kalman filter based on improved matrix decomposition methods for lithium-ion battery state of charge estimation

Accurately predicting the state of charge (SOC) is crucial to improving Li-ion battery performance. However, available model-based estimation approaches still face challenges in handling model uncertainty and measurement noise effects on parameter identification. Besides, the widely used unscented Kalman filter (UKF) algorithm has limitations in electric vehicles as it requires the error covariance matrix to maintain positive definiteness, limiting its applicability under certain conditions. This study introduces the bias-compensated forgetting factor recursive least squares (BCFFRLS) method for parameter estimation within the second-order RC equivalent circuit model specific to the INR18650-20R battery. Furthermore, we propose a novel algorithm named the optimization multi-interest adaptive unscented Kalman filter (O-MIAUKF). This algorithm is designed to address stability and robustness issues with traditional UKF encounters in dynamic environments. Experimental validation demonstrates that the O-MIAUKF algorithm excels in maintaining strong stability and robustness in various working conditions, accurately estimating SOC even with a non-positive covariance matrix. The SOC estimation error remains stable at 0.8 %, which is lower than that of the current Extended Kalman Filter (EKF), UKF, and Dual Extended Kalman Filter (DEKF).

Fraction order particle swarm optimization for parameter extraction of triple-diode photovoltaic models

This work proposes an application of Fractional Order Particle Swarm Optimization (FO-PSO), a meta-heuristic method for parameters estimation of photo-voltaic (PV) module as a non-linear, transcendental, multi-modal and implicit optimization problem. The uses single diode model (SDM), double diode model (DDM) and three-diode model (TDM) of PV modules with the constraint that only data-sheet information may be utilized. A fitness function based on the error amongst the computed values of current and voltage and the ones given in characteristic I-V curves of data-sheet, is minimized using the FO-PSO to get the required parameters. The comparative study between the estimated and data-sheet values provided by PV module manufacturers will determine the effectiveness of this research. The effectiveness of the FO-PSO is demonstrated by comparing the fitness values with that of other techniques. The FO-PSO technique makes a novel contribution to the PV power systems industry by making it possible to obtain a nearly realistic model of any commercial PV module. The effectiveness of the FO-PSO is determined by comparing the results for all the three models with the state of the art optimization techniques. The Root Mean Square error values calculated for the TDM is less than 10e–16, producing very consistent FO-PSO results. Therefore, FO-PSO is anticipated to be a competitive method for obtaining PV module specifications.

A Multistage Nonlinear Method for Aeroengine Health Parameter Estimation Based on Adjacent Operating Points

Abstract Health parameter estimation is the core of engine gas path analysis (GPA), which is widely adopted for engine safety improvement, as well as for operation and maintenance cost reduction. The major challenge of GPA lies in the contradiction between the high dimensions of parameters under estimation, e.g., health parameters, and the limited measurements obtainable from a small number of sensors. Existent GPA methods for health parameters commonly apply dimension reduction before estimation, leading to information loss and hence inaccurate estimation. To tackle the challenge of limited sensor measurements and to have more system outputs than parameters under estimation, we proposed to augment the output vector of the system model by combining the measurements from multiple adjacent operating points. But the engine model can face the problem of homogenization if using data from adjacent operating points. This can, in turn, lead to a low identifiability of parameters. We analyze the internal mechanism of such large deviation of the parameter estimation results based on linear models and argue for the need of nonlinear method. Hence, we propose a multistage nonlinear parameter estimation method for health parameters, combining biased and unbiased estimation. In our extensive simulations based on 10 output measurements of a JT9D engine, our method can estimate 130% more parameters than the widely used GPA method, while reducing the maximum estimation error of health parameters from 2.2% to 0.1%.

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by electrochemical sensors can be used to offer quantitative information about drug concentrations. Several approaches have been proposed for single-concentration quantification based on CV results and machine learning (ML) models with lower training difficulty. However, insufficient measured dataset hinders the application of diverse large-scale ML algorithms in this field. A new method for automatic parameter estimation by ML of measured CV samples is here illustrated with the aim to generate a large simulated dataset for generalized drug concentration quantification model training in this paper. We present an ML-based approach that combines k-means clustering, polynomial regression, and Gaussian Mixture Model (GMM), which automates parameter estimation and simulation using peak detection, baseline subtraction, and peak Gaussian fitting with a small number of measurements. Large simulation datasets constructed on the basis of the estimation results open the possibility of training ML models for more generalized drug concentration quantification. The simulated dataset is processed to assess the efficiency of the proposed method. The Mean Average Percentage Error (MAPE) was 0.32% for etoposide (ETO) and 4.78% for methotrexate (MTX).

Uncertain process-based data integration and residual lifetime evaluation of PCB in airborne equipment with ADT and field data

Accurately evaluating the lifespan of the Printed Circuit Board (PCB) in airborne equipment is an essential issue for aircraft design and operation in the marine atmospheric environment. This paper presents a novel evaluation method by fusing Accelerated Degradation Testing (ADT) data, degradation data, and life data of small samples based on the uncertainty degradation process. An uncertain life model of PCB in airborne equipment is constructed by employing the uncertain distribution that considers the accelerated factor of multiple environmental conditions such as temperature, humidity, and salinity. In addition, a degradation process model of PCB in airborne equipment is constructed by employing the uncertain process of fusing ADT data and field data, in which the performance characteristics of dynamic cumulative change are included. Based on minimizing the pth sample moments, an integrated method for parameter estimation of the PCB in airborne equipment is proposed by fusing the multi-source data of life, degradation, and ADT. An engineering case illustrates the effectiveness and advantage of the proposed method.

Analytical Gaussian process cosmography: unveiling insights into matter-energy density parameter at present

In this study, we introduce a novel analytical Gaussian Process (GP) cosmography methodology, leveraging the differentiable properties of GPs to derive key cosmological quantities analytically. Our approach combines cosmic chronometer (CC) Hubble parameter data with growth rate (f) observations to constrain the Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}$$\\end{document} parameter, offering insights into the underlying dynamics of the Universe. By formulating a consistency relation independent of specific cosmological models, we analyze under a flat FLRW metric and first-order Newtonian perturbation theory framework. Our analytical approach simplifies the process of Gaussian Process regression (GPR), providing a more efficient means of handling large datasets while offering deeper interpretability of results. We demonstrate the effectiveness of our methodology by deriving precise constraints on Ωm0h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}h^2$$\\end{document}, revealing Ωm0h2=0.139±0.017\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _\ extrm{m0}h^2=0.139\\pm 0.017$$\\end{document}. Moreover, leveraging H0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_0$$\\end{document} observations, we further constrain Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}$$\\end{document}, uncovering an inverse correlation between mean H0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_0$$\\end{document} and Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\ extrm{m0}}$$\\end{document}. Our investigation offers a proof of concept for analytical GP cosmography, highlighting the advantages of analytical methods in cosmological parameter estimation.

Bayesian predictive modeling for gas purification using breakthrough curves

This study proposes a predictive model for assessing adsorber performance in gas purification processes, specifically targeting the removal of chemical warfare agents (CWAs) using breakthrough curve analysis. Conventional parameter estimation methods, such as Brunauer-Emmett-Teller analysis, encounter challenges due to the limited availability of kinetic and equilibrium data for CWAs. To overcome these challenges, we implement a Bayesian parametric inference method, facilitating direct parameter estimation from breakthrough curves. The model’s efficacy is confirmed by applying it to H2S purification in a fixed-bed setup, where predicted breakthrough curves aligned closely with previous experimental and numerical studies. Furthermore, the model is applied to sarin with ASZM-TEDA carbon, estimating key parameters that could not be assessed through conventional experimental techniques. The reconstructed breakthrough curves closely match actual measurements, highlighting the model’s accuracy and robustness. This study not only enhances filter performance prediction for CWAs but also offers a streamlined approach for evaluating gas purification technologies under limited experimental data conditions.

A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27 % of the total area of the Tibetan Plateau and is also one of the most important vegetation types. The Dangxiong alpine meadow ecosystem, located in the south-central part of the Tibetan Plateau, is a typical example. To understand the carbon and water fluxes, water use efficiency (WUE), and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area, two parameter estimation methods, the Model-independent Parameter Estimation (PEST) and the Dynamic Dimensions Search (DDS), were used to optimize the Biome-BGC model. Then, the gross primary productivity (GPP) and evapotranspiration (ET) were simulated. The results show that the DDS parameter calibration method has a better performance. The annual GPP and ET show an increasing trend, while the WUE shows a decreasing trend. Meanwhile, ET and GPP reach their peaks in July and August, respectively, and WUE shows a “dual-peak” pattern, reaching peaks in May and November. Furthermore, according to the simulation results for the next nearly 100 years, the ensemble average GPP and ET exhibit a significant increasing trend, and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario. WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario. This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.摘要全球气候变化对青藏高原生态系统产生了深远影响, 暖湿化背景下青藏高原植被碳, 水通量变化趋势值得关注. 高寒草甸是青藏高原最主要的植被类型之一, 为理解青藏高原当雄地区高寒草甸生态系统碳, 水通量, 水分利用效率及其对未来气候变化的响应, 本研究利用PEST和DDS两种参数率定方法优化Biome-BGC模型, 进而模拟2000–2019年当雄站的总初级生产力 (GPP) 和蒸散量 (ET) . 研究结果表明: DDS参数率定方法具有更优的性能. GPP和ET在研究时段内呈上升趋势, 而水分利用效率 (WUE) 则呈下降趋势. 同时, ET和GPP分别在7月和8月达到峰值, 而WUE则呈“双峰”变化, 分别于5月和11月达到峰值. 此外, 未来近百年的模拟表明GPP和ET的集合平均结果呈显著增加趋势, 其中在SSP5–8.5情景下的增速大于SSP2–4.5情景. WUE在SSP2–4.5情景下呈增加趋势, 而在SSP5–8.5情景下呈显著增加趋势. 本研究结果可为青藏高原碳, 水循环预测研究和植被生态保护的应用研究提供参考和借鉴.

Solving rician data analysis problems: theory and numerical modeling using computer algebra metods in Wolfram Mathematica

This paper considers theoretical foundations and mathematical methods of data analysis under the conditions of the Rice statistical distribution. The problem involves joint estimation of the signal and noise parameters. It is shown that this estimation requires the solution of a complex system of essentially nonlinear equations with two unknown variables, which implies significant computational costs. This study is aimed at mathematical optimization of computer algebra methods for numerical solution of the problem of Rician data analysis. As a result of the optimization, the solution of the system of two nonlinear equations is reduced to the solution of one equation with one unknown variable, which significantly simplifies algorithms for the numerical solution of the problem, reduces the amount of necessary computational resources, and enables the use of advanced methods for parameter estimation in information systems with priority of real-time operation. Results of numerical experiments carried out using Wolfram Mathematica confirm the effectiveness of the developed methods for two-parameter analysis of Rician data. The data analysis methods considered in this paper are useful for solving many scientific and applied problems that involve analysis of data described by the Rice statistical model.

New Weibull Log-Logistic grey forecasting model for a hard disk drive failures

Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181 %, 2.2306 %, 8.1712 %, and 4.3417 %, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives.

Toward sustainable climate action in advanced economies: Linking information communication technology, technological innovation, economic complexity, and ecological footprint

AbstractInformation and communication technology (ICT), technological innovation, and renewable energy (REN) consumption have been proffered as solutions to the recent environmental tragedies in developed countries. In recent times, ICT diffusion and technological innovation have improved in G7 countries, but the same cannot be said of REN consumption. As such, this study examines the link between ICT, economic complexity, technological innovation, REN, and ecological footprint (EF) for G7 countries over the period 1990–2020. We use three variables (fixed telephone subscriptions [FTS], mobile cellular subscriptions [MCS], and individuals using the internet [IUI]) to represent ICT. The presence of cross‐sectional dependence guides the use of second‐generation econometric methods for slope heterogeneity, unit root, cointegration, and parameter estimation. The augment mean group (AMG) estimator and panel OLS techniques are applied to complement the method of moment quantile regression (MM‐QR) approach. The MM‐QR results suggest that REN consumption and technological innovation impede the EF across all quantile levels (0.1–0.9), whereas economic growth and economic complexity augment the EF in G7 countries. The ICT variables have heterogeneous effects on the EF, suggesting that the impact of ICT on the EF depends on the estimation techniques and proxy for the variable. In line with these outcomes, public policies directed toward funding technological innovation projects are recommended. The funding should specifically focus on environmentally friendly technologies that can guarantee complementarity between reduced environmental damage and increased economic growth.

Estimation of Weibull Probability Distribution Parameters with Optimization Algorithms and Foça Wind Data Application

In this study, the scale and shape parameters of the Weibull probability distribution function (W.pdf) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foça region of İzmir in Turkey. The values of Weibull parameters obtained using Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Social Group Optimization (SGO), and Bat Algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (χ^2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foça were used. As a result of Foça speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and χ^2 criteria are evaluated together; it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

A two-step item bank calibration strategy based on 1-bit matrix completion for small-scale computerized adaptive testing.

Computerized adaptive testing (CAT) is a widely embraced approach for delivering personalized educational assessments, tailoring each test to the real-time performance of individual examinees. Despite its potential advantages, CAT�s application in small-scale assessments has been limited due to the complexities associated with calibrating the item bank using sparse response data and small sample sizes. This study addresses these challenges by developing a two-step item bank calibration strategy that leverages the 1-bit matrix completion method in conjunction with two distinct incomplete pretesting designs. We introduce two novel 1-bit matrix completion-based imputation methods specifically designed to tackle the issues associated with item calibration in the presence of sparse response data and limited sample sizes. To demonstrate the effectiveness of these approaches, we conduct a comparative assessment against several established item parameter estimation methods capable of handling missing data. This evaluation is carried out through two sets of simulation studies, each featuring different pretesting designs, item bank structures, and sample sizes. Furthermore, we illustrate the practical application of the methods investigated, using empirical data collected from small-scale assessments.

Optimal Parameter Estimation Techniques for Complex Nonlinear Systems

AbstractAccurate parameter estimation and state identification within nonlinear systems are fundamental challenges addressed by optimization techniques. This paper fills a critical gap in previous research by investigating tailored optimization methods for parameter estimation in nonlinear system modeling, with a particular emphasis on chaotic dynamical systems. We introduce and compare three optimization methods: a gradient-based iterative algorithm, the Levenberg-Marquardt algorithm, and the Nelder-Mead simplex method. These methods are strategically employed to simplify complex nonlinear optimization problems, rendering them more manageable. Through a comprehensive exploration of the performance of these methods in determining parameters across diverse systems, including the van der Pol oscillator, the Rössler system, and pharmacokinetic modeling, our study revealed that the accuracy and reliability of the Nelder-Mead simplex method were consistent. The Nelder-Mead simplex algorithm emerged as a powerful tool, that consistently outperforms alternative methods in terms of root mean squared error (RMSE) and convergence reliability. Visualizations of trajectory comparisons and parameter convergence under various noise levels further emphasize the algorithm’s robustness. These studies suggest that the Nelder-Mead simplex method has potential as a valuable tool for parameter estimation in chaotic dynamical systems. Our study’s implications extend beyond theoretical considerations, offering promising insights for parameter estimation techniques in diverse scientific fields reliant on nonlinear system modeling.

A Comprehensive Study on Model Simplification and Parameter Estimation of Brushless Doubly Fed Standalone Generation System

Brushless doubly-fed induction generator (BDFIG) has great potential in stand-alone generation applications. Currently, lots of control strategies have been proposed based on the BDFIG d-q model with different machine parameters ignored for simplification, but the simplification influences on model accuracy have not been discussed systematically, and the application ranges are not clear. Therefore, aiming at controller design, this paper provides a comprehensive study on model simplification and parameter estimation of brushless doubly-fed stand- alone generation. Based on the intuitive DFIG-like BDFIG model and the typical electrical quantity relations widely used in stand-alone controller design, steady-state and dynamic parameter sensitivities of BDFIG are analyzed with operation condition fully considered. Based on the analysis, the universal BDFIG simplified d-q model aiming at stand-alone control is evaluated sufficiently, and the application range is clarified. The simplification of control analysis is illustrated, and a highly-targeted and simple parameter estimation method is proposed for simplified modeling and control. Finally, simulations and experiments verify the analysis accuracy of the simplified DFIG-like BDFIG model and the feasibility of the proposed parameter estimation method.