IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i4d10.1007_s13198-021-01614-w.html
   My bibliography  Save this article

Performance analysis of novel robust ANN-MRAS observer applied to induction motor drive

Author

Listed:
  • Weam EL Merrassi

    (Sultan Moulay Slimane University)

  • Abdelouahed Abounada

    (Sultan Moulay Slimane University)

  • Mohamed Ramzi

    (Sultan Moulay Slimane University)

Abstract

This paper presents a novel method for estimating the rotor speed of a sensorless indirect field-oriented control (IFOC) induction motor based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to enhance the conventional MRAS performance especially in low-speed regions, and to reduce its sensitivity to noise and system uncertainties. For this purpose, an advanced MRAS has been involved to estimate the rotor speed with artificial intelligence (AI) approach, with the aim of achieving a high-performance of vector-controlled induction machine drive. The adjustable and reference models are designed based on an artificial neural network (ANN) structure in an attempt to estimate speed and rotor flux out of the measured terminal voltages and currents. The ANN structure promised eradication of pure integration with immunity to parameter variation with extreme-precision. Accordingly, some simulation results are presented to validate the proposed method and to highlight the performance analysis of the improved Neural Network rotor flux MRAS (NN RF-MRAS) observer compared to the conventional MRAS observer. The effectiveness of the proposed observer has been carried out under different operating conditions, based on benchmark tests using MATLAB/Simulink software environment.

Suggested Citation

  • Weam EL Merrassi & Abdelouahed Abounada & Mohamed Ramzi, 2022. "Performance analysis of novel robust ANN-MRAS observer applied to induction motor drive," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 2011-2028, August.
  • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01614-w
    DOI: 10.1007/s13198-021-01614-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01614-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-021-01614-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aman A. Tanvir & Adel Merabet, 2020. "Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid," Energies, MDPI, vol. 13(7), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

    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. Marinka Baghdasaryan & Azatuhi Ulikyan & Arusyak Arakelyan, 2023. "Application of an Artificial Neural Network for Detecting, Classifying, and Making Decisions about Asymmetric Short Circuits in a Synchronous Generator," Energies, MDPI, vol. 16(6), pages 1-19, March.
    2. Mohammad Soleymannejad & Danial Sadrian Zadeh & Behzad Moshiri & Ebrahim Navid Sadjadi & Jesús García Herrero & Jose Manuel Molina López, 2022. "State Estimation Fusion for Linear Microgrids over an Unreliable Network," Energies, MDPI, vol. 15(6), pages 1-24, March.
    3. Malgorzata Binek & Andrzej Kanicki & Pawel Rozga, 2021. "Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System," Energies, MDPI, vol. 14(9), pages 1-14, April.
    4. Yanis Hamoudi & Hocine Amimeur & Djamal Aouzellag & Maher G. M. Abdolrasol & Taha Selim Ustun, 2023. "Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System," Energies, MDPI, vol. 16(12), pages 1-19, June.
    5. Adolfo Dannier & Emanuele Fedele & Ivan Spina & Gianluca Brando, 2022. "Doubly-Fed Induction Generator (DFIG) in Connected or Weak Grids for Turbine-Based Wind Energy Conversion System," Energies, MDPI, vol. 15(17), pages 1-5, September.
    6. José Antonio Cortajarena & Oscar Barambones & Patxi Alkorta & Jon Cortajarena, 2021. "Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid," Mathematics, MDPI, vol. 9(2), pages 1-18, January.
    7. Mohammadali Kiehbadroudinezhad & Adel Merabet & Homa Hosseinzadeh-Bandbafha, 2021. "Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration," Energies, MDPI, vol. 14(13), pages 1-20, June.

    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:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01614-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.