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Relevant bibliographies by topics / Thevenin model Identification of parameters

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Academic literature on the topic 'Thevenin model Identification of parameters'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Thevenin model Identification of parameters"

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Zhang, Liang, Shunli Wang, Daniel-Ioan Stroe, Chuanyun Zou, Carlos Fernandez, and Chunmei Yu. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries." Energies 13, no. 8 (2020): 2057. http://dx.doi.org/10.3390/en13082057.

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An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working cha

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Khalfi, Jaouad, Najib Boumaaz, Abdallah Soulmani, and El Mehdi Laadissi. "An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 2798. http://dx.doi.org/10.11591/ijece.v11i4.pp2798-2810.

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The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflecti

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Han, X., Y.-J. Guo, Y.-E. Zhao, and Z.-Q. Lin. "The application of power-based transfer path analysis to passenger car structure-borne noise." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 222, no. 11 (2008): 2011–23. http://dx.doi.org/10.1243/09544070jauto750.

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Structure-borne noise in a passenger car is usually transmitted through multiple and/or multi-dimensional paths. Therefore, identification and control of these transfer paths are effective measures for noise reduction. A power-based transfer path analysis methodology is proposed for this purpose. First, the power flow of each transfer path is estimated with an equivalent-uncoupled-system method based on linear network theory and the Thevenin equivalent theorem. Next, the correlation between the power flow of each transfer path and the sound pressure in the passenger compartment is established;

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Zhang, Yuwei, Wenying Liu, Fangyu Wang, Yaoxiang Zhang, and Yalou Li. "Reactive Power Control Method for Enhancing the Transient Stability Total Transfer Capability of Transmission Lines for a System with Large-Scale Renewable Energy Sources." Energies 13, no. 12 (2020): 3154. http://dx.doi.org/10.3390/en13123154.

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With the increased proportion of intermittent renewable energy sources (RES) integrated into the sending-end, the total transfer capability of transmission lines is not sufficient during the peak periods of renewable primary energy (e.g., the wind force), causing severe RES power curtailment. The total transfer capability of transmission lines is generally restricted by the transient stability total transfer capability (TSTTC). This paper presents a reactive power control method to enhance the TSTTC of transmission lines. The key is to obtain the sensitivity between TSTTC and reactive power, w

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Wei, Ke Xin, and Qiao Yan Chen. "Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter." Advanced Materials Research 403-408 (November 2011): 2211–15. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2211.

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This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

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Li, Shaowu. "Circuit Parameter Range of Photovoltaic System to Correctly Use the MPP Linear Model of Photovoltaic Cell." Energies 14, no. 13 (2021): 3997. http://dx.doi.org/10.3390/en14133997.

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The real-time linearization of a photovoltaic (PV) cell has been implemented well by the proposition of two maximum power point (MPP) linear models (MPP Thevenin cell model and MPP Norton cell model). However, there is no work to specially analyze the circuit parameter range (CPR) to correctly use them, which seriously impedes the development of the linear control theory involving them. To deal with this problem, in this paper, PV systems with three usual outputs are analyzed and the expressions of their CPR are proposed under ideal conditions. Meanwhile, these expressions are improved to matc

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Xiong, Rui, Hongwen He, and Kai Zhao. "Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach." International Journal of Green Energy 12, no. 3 (2014): 272–78. http://dx.doi.org/10.1080/15435075.2014.891512.

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Liu, Xintian, Xuhui Deng, Yao He, Xinxin Zheng, and Guojian Zeng. "A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries." Energies 13, no. 1 (2019): 121. http://dx.doi.org/10.3390/en13010121.

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With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected

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Bao, Hui, Wei Jiang, and Dan Wei. "Electric Vehicle Battery SOC Estimation Based on EKF." Advanced Materials Research 926-930 (May 2014): 927–31. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.927.

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In order to estimate the battery state of charge (SOC) accurately, an improved Thevenin model of a battery is established, its mathematical relation is very simple, and also it is easy to realize. In addition, we identify the model parameters, and then use extended Calman filter algorithm to estimate the battery state of charge. The simulation results show that, this model can well reflect the dynamic and static characteristics of a battery, and the Calman algorithm can keep good accuracy in the estimation process.

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Wang, Hao, Yanping Zheng, and Yang Yu. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter." Mathematics 9, no. 15 (2021): 1733. http://dx.doi.org/10.3390/math9151733.

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In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the

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Dissertations / Theses on the topic "Thevenin model Identification of parameters"

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Loucký, Vojtěch. "Model Li-ion akumulátoru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442783.

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This diploma thesis deals with description of the principle of Li-ion cells, literature search on the topic of mathematical models of Li-ion cells and the creation of a selected mathematical model in MATLAB, which is able to simulate the course of voltage and state of charge as a function of time for different ambient conditions, such as various aging of battery .The creation of both the model and the procedure of identification of parameters necessary for the creation of the model are described here as well as different options of identification of parameters. The selected Thevenin model is t

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Bornitz, Matthias, Thomas Zahnert, Hans-Jürgen Hardtke, and Karl-Bernd Hüttenbrink. "Identification of Parameters for the Middle Ear Model." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-135790.

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This paper presents a method of parameter identification for a finite-element model of the human middle ear. The parameter values are estimated using a characterization of the difference in natural frequencies and mode shapes of the tympanic membrane between the model and the specimens. Experimental results were obtained from temporal bone specimens under sound excitation (300–3,000 Hz). The first 3 modes of the tympanic membrane could be observed with a laser scanning vibrometer and were used to estimate the stiffness parameters for the orthotropic finite-element model of the eardrum. A furth

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Bornitz, Matthias, Thomas Zahnert, Hans-Jürgen Hardtke, and Karl-Bernd Hüttenbrink. "Identification of Parameters for the Middle Ear Model." Karger, 1999. https://tud.qucosa.de/id/qucosa%3A27677.

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This paper presents a method of parameter identification for a finite-element model of the human middle ear. The parameter values are estimated using a characterization of the difference in natural frequencies and mode shapes of the tympanic membrane between the model and the specimens. Experimental results were obtained from temporal bone specimens under sound excitation (300–3,000 Hz). The first 3 modes of the tympanic membrane could be observed with a laser scanning vibrometer and were used to estimate the stiffness parameters for the orthotropic finite-element model of the eardrum. A furth

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Kučerová, Anna. "Identification of nonlinear mechanical model parameters based on softcomputing methods." Cachan, Ecole normale supérieure, 2007. http://tel.archives-ouvertes.fr/tel-00256025/fr/.

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Le problème d'identification des paramètres apparaît dans beaucoup de problèmes en génie civil sous formes différentes et il peut être résolu par beaucoup de méthodes distinctes. Cette thèse présente deux philosophies principales d'identification avec orientation vers les méthodes basées sur intelligence artificielle. Les aspects pratiques sont montrés sur plusieurs problèmes d'identification, où les paramètres des modèles mécaniques non linéaires sont à déterminer<br>The problem of parameters identification occurs in many engineering tasks and, as such, attains several différent forms and can

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Temeltas, H. "Real-time identification of robot dynamic model parameters using parallel processing." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357973.

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Karameh, Fadi Nabih. "On-line identification and control algorithm for system model with jump parameters using wavelets." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11019.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (leaves 73-75).<br>by Fadi Nabih Karameh.<br>M.S.

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Zhou, Haiyan. "Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/12267.

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La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las

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Benedetti, Lorenzo. "Substructuring approache in state space models for dynamic system parameters identification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amslaurea.unibo.it/2325/.

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In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the

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Huang, Changwu. "Kriging-assisted evolution strategy for optimization and application in material parameters identification." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMIR05.

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Afin de réduire le coût de calcul pour des problèmes d'optimisation coûteuse, cette thèse a été consacrée à la Stratégie d'Evolution avec Adaptation de Matrice de Covariance assistée par modèle de Krigeage (KA-CMA-ES). Plusieurs algorithmes de KA-CMA-ES ont été développés et étudiés. Une application de ces algorithmes KA-CMA-ES développés est réalisée par l'identification des paramètres matériels avec un modèle constitutif d'endommagement élastoplastique. Les résultats expérimentaux démontrent que les algorithmes KA-CMA-ES développés sont plus efficaces que le CMA-ES standard. Ils justifient a

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Spohrer, Klaus. "The water regime in a lychee orchard of Northern Thailand : identification of model parameters for water balance modelling /." Stuttgart : Univ. Hohenheim, Inst. für Bodenkunde und Standortlehre, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016421055&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Books on the topic "Thevenin model Identification of parameters"

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial a

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Book chapters on the topic "Thevenin model Identification of parameters"

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Gorokhovski, Vikenti. "Model Identification." In Effective Parameters of Hydrogeological Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03569-7_4.

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Kozlowski, Krzysztof. "Identification of robot model parameters." In Advances in Industrial Control. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-0429-2_4.

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Doyle, F. J., R. K. Pearson, and B. A. Ogunnaike. "Determination of Volterra Model Parameters." In Identification and Control Using Volterra Models. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0107-9_4.

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Cambraia, Heraldo N., Leonardo M. L. Contini, and Paulo R. G. Kurka. "Operational Modal Parameters Identification Using the ARMAV Model." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91217-2_11.

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Fardmoshiri, M., M. Sasso, E. Mancini, G. Chiappini, and M. Rossi. "Identification of Constitutive Model Parameters in Hopkinson Bar Tests." In Residual Stress, Thermomechanics & Infrared Imaging, Hybrid Techniques and Inverse Problems, Volume 9. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42255-8_23.

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Hong, Q. Y., Sam Kwong, and H. L. Wang. "Optimization of Gaussian Mixture Model Parameters for Speaker Identification." In Genetic and Evolutionary Computation – GECCO 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_141.

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Zou, Hang, Wei Zhang, Junyi Zuo, Xiaodan Chen, and Yawen Cao. "EM-Based Online Identification Algorithm for Linear Aerodynamic Model Parameters." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3305-7_182.

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Van den Hof, Paul M. J., Jorn F. M. Van Doren, and Sippe G. Douma. "Identification of Parameters in Large Scale Physical Model Structures, for the Purpose of Model-Based Operations." In Model-Based Control:. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0895-7_8.

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Yamamoto, Takashi, Shinichi Maruyama, Kazuhiro Izui, and Shinji Nishiwaki. "Identification of Material Parameters in Biot’s Model by the Homogenization Method." In Topics in Modal Analysis II, Volume 6. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2419-2_5.

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Braiek, Sonia, Ated Ben Khalifa, Redouane Zitoune, and Mondher Zidi. "Model Parameters Identification of Adhesively Bonded Composites Tubes Under Internal Pressure." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27146-6_65.

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Conference papers on the topic "Thevenin model Identification of parameters"

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Peng, Wei, Zhengqiu Yang, Chen Liu, Jiapeng Xiu, and Zheng Zhang. "An Improved PSO Algorithm for Battery Parameters Identification Optimization Based on Thevenin Battery Model." In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691341.

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Locorotondo, Edoardo, Luca Pugi, Lorenzo Berzi, Marco Pierini, and Giovanni Lutzemberger. "Online Identification of Thevenin Equivalent Circuit Model Parameters and Estimation State of Charge of Lithium-Ion Batteries." In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2018. http://dx.doi.org/10.1109/eeeic.2018.8493924.

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Huang, Cong-Sheng, and Mo-Yuen Chow. "Accurate Thevenin's circuit-based battery model parameter identification." In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7744902.

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Chen, Xinnan, Yuan Sun, and Rui Yin. "An Improvement Algorithm for Online Identification of Thevenin Equivalent Parameters." In 2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, 2021. http://dx.doi.org/10.1109/aeees51875.2021.9403072.

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Xinyuan, Meng, Wen Tao, and Ma Kaigang. "Application of Python Parallel Computing in Online Identification of Thevenin Equivalent Parameters." In 2020 IEEE Student Conference on Electric Machines and Systems (SCEMS). IEEE, 2020. http://dx.doi.org/10.1109/scems48876.2020.9352354.

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Liu, Youbo, Zhuoyi Li, Yue Yang, and Junyong Liu. "A novel on-line identification for Thevenin equivalent parameters of power system regarding persistent disturbance condition." In 2016 China International Conference on Electricity Distribution (CICED). IEEE, 2016. http://dx.doi.org/10.1109/ciced.2016.7575909.

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Jianwei Zhao, Zheng Yan, Lu Cao, and Jianhua Li. "Study on Thevenin equivalent model and algorithm of AC/DC power systems for voltage instability identification." In 2014 International Conference on Power System Technology (POWERCON). IEEE, 2014. http://dx.doi.org/10.1109/powercon.2014.6993794.

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ALLEN, JAMES, and DAVID MARTINEZ. "Automating the identification of structural model parameters." In 30th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1242.

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Vesely, Ivo, and Lukas Pohl. "Parameters identification of PMSM through Hammerstein model." In IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2013. http://dx.doi.org/10.1109/iecon.2013.6699612.

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Cipin, Radoslav, Marek Toman, Petr Prochazka, and Ivo Pazdera. "Identification of Li-ion Battery Model Parameters." In 2019 International Conference on Electrical Drives & Power Electronics (EDPE). IEEE, 2019. http://dx.doi.org/10.1109/edpe.2019.8883926.

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