IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i3p611-d486907.html
   My bibliography  Save this article

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

Author

Listed:
  • Artun Sel

    (Department of Electrical-Electronics Engineering, TOBB University of Economics and Technology, 06510 Ankara, Turkey)

  • Bilgehan Sel

    (Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey)

  • Cosku Kasnakoglu

    (Department of Electrical-Electronics Engineering, TOBB University of Economics and Technology, 06510 Ankara, Turkey)

Abstract

In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.

Suggested Citation

  • Artun Sel & Bilgehan Sel & Cosku Kasnakoglu, 2021. "GLSDC Based Parameter Estimation Algorithm for a PMSM Model," Energies, MDPI, vol. 14(3), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:611-:d:486907
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/3/611/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/3/611/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hui-Juan Xu & Yun-Ze Li & Li-Jun Gao & Xin Zhang, 2020. "Planned Heating Control Strategy and Thermodynamic Modeling of a Natural Gas Thermal Desorption System for Contaminated Soil," Energies, MDPI, vol. 13(3), pages 1-28, February.
    2. Seungpil Jung & Kyungbook Lee & Changhyup Park & Jonggeun Choe, 2018. "Ensemble-Based Data Assimilation in Reservoir Characterization: A Review," Energies, MDPI, vol. 11(2), pages 1-23, February.
    3. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
    4. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    5. Seungpil Jung, 2017. "Integration of the Production Logging Tool and Production Data for Post-Fracturing Evaluation by the Ensemble Smoother," Energies, MDPI, vol. 10(7), pages 1-12, June.
    6. Dingyu Wang & Yiguang Chen, 2020. "Fault-Tolerant Control of Coil Inter-Turn Short-Circuit in Five-Phase Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 13(21), pages 1-19, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruipeng Guo & Lilan Dong & Hao Wu & Fangdi Hou & Chen Fang, 2021. "A Practical GERI-Based Method for Identifying Multiple Erroneous Parameters and Measurements Simultaneously," Energies, MDPI, vol. 14(12), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Jingwen & Chen, Chunlin, 2021. "A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries," Energy, Elsevier, vol. 229(C).
    2. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    3. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    4. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    5. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    6. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    7. Saleh Mohammed Shahriar & Erphan A. Bhuiyan & Md. Nahiduzzaman & Mominul Ahsan & Julfikar Haider, 2022. "State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence," Energies, MDPI, vol. 15(21), pages 1-26, October.
    8. Seungpil Jung & Kyungbook Lee & Changhyup Park & Jonggeun Choe, 2018. "Ensemble-Based Data Assimilation in Reservoir Characterization: A Review," Energies, MDPI, vol. 11(2), pages 1-23, February.
    9. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    10. Tian, Zhongda, 2020. "Chaotic characteristic analysis of network traffic time series at different time scales," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    11. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    12. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    13. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
    14. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
    15. Ni, Zichuan & Xiu, Xianchao & Yang, Ying, 2022. "Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis," Energy, Elsevier, vol. 254(PC).
    16. Dongmei Zhang & Yuyang Zhang & Bohou Jiang & Xinwei Jiang & Zhijiang Kang, 2020. "Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching," Energies, MDPI, vol. 13(17), pages 1-15, August.
    17. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
    18. Jichao Hong & Fengwei Liang & Xun Gong & Xiaoming Xu & Quanqing Yu, 2022. "Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network," Energies, MDPI, vol. 15(24), pages 1-14, December.
    19. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    20. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:611-:d:486907. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.