IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p8834-d981472.html
   My bibliography  Save this article

Improved Moth Flame Optimization Approach for Parameter Estimation of Induction Motor

Author

Listed:
  • Zekharya Danin

    (Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, Israel)

  • Abhishek Sharma

    (Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun 248007, India)

  • Moshe Averbukh

    (Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, Israel)

  • Arabinda Meher

    (University Centre for Research & Development, Chandigarh University, Mohali 140413, India)

Abstract

The effective deployment of electrical energy has received attention because of its environmental implications. On the other hand, induction motors are the primary equipment used in many industries. Industrial facilities demand the maximum percentage of energy. This energy demand is determined by the operating circumstances imposed by the internal characteristics of the induction motor. Because internal parameters of an induction motor are not immediately measurable, they must be obtained through an identification process. This paper proposed an improved version of moth flame optimization (IMFO) for the efficient parameter estimation of induction motors. A steady-state equivalent circuit of the induction motor is employed for the simulation. The proposed technique handles the parameter estimation problem better than moth flame optimization (MFO), particle swarm optimization (PSO), the flower pollination algorithm (FPA), the tunicate swarm algorithm (TSA), and the sine cosine algorithm (SCA). The anticipated IMFO reduces the cost function by 49.38% as compared with the basic version of MFO.

Suggested Citation

  • Zekharya Danin & Abhishek Sharma & Moshe Averbukh & Arabinda Meher, 2022. "Improved Moth Flame Optimization Approach for Parameter Estimation of Induction Motor," Energies, MDPI, vol. 15(23), pages 1-13, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8834-:d:981472
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/8834/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/23/8834/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
    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. Xinbiao Wang & Yuxuan Du & Zhuozhuo Tu & Yong Luo & Xiao Yuan & Dacheng Tao, 2024. "Transition role of entangled data in quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    2. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    3. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.
    4. Hegazy Rezk & Abdul Ghani Olabi & Rania M. Ghoniem & Mohammad Ali Abdelkareem, 2023. "Optimized Fractional Maximum Power Point Tracking Using Bald Eagle Search for Thermoelectric Generation System," Energies, MDPI, vol. 16(10), pages 1-15, May.
    5. Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).
    6. Hernán Peraza-Vázquez & Adrián Peña-Delgado & Prakash Ranjan & Chetan Barde & Arvind Choubey & Ana Beatriz Morales-Cepeda, 2021. "A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade," Mathematics, MDPI, vol. 10(1), pages 1-32, December.
    7. Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Giovanni Giachetti & Álex Paz & Alvaro Peña Fritz, 2024. "Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics," Mathematics, MDPI, vol. 12(2), pages 1-39, January.
    8. Alaa A. K. Ismaeel & Essam H. Houssein & Doaa Sami Khafaga & Eman Abdullah Aldakheel & Ahmed S. AbdElrazek & Mokhtar Said, 2023. "Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    9. Kottath, Rahul & Singh, Priyanka, 2023. "Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem," Energy, Elsevier, vol. 263(PC).
    10. Mokhtar Said & Ali M. El-Rifaie & Mohamed A. Tolba & Essam H. Houssein & Sanchari Deb, 2021. "An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem," Mathematics, MDPI, vol. 9(21), pages 1-14, November.
    11. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    12. Satya Verma & Satya Prakash Sahu & Tirath Prasad Sahu, 2024. "Two-Stage Hybrid Feature Selection Approach Using Levy’s Flight Based Chicken Swarm Optimization for Stock Market Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2193-2224, June.
    13. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & José García, 2022. "Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector," Mathematics, MDPI, vol. 10(24), pages 1-30, December.
    14. Alexander Gerling & Holger Ziekow & Andreas Hess & Ulf Schreier & Christian Seiffer & Djaffar Ould Abdeslam, 2022. "Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 555-573, February.
    15. Pan, Jeng-Shyang & Zhang, Zhen & Chu, Shu-Chuan & Zhang, Si-Qi & Wu, Jimmy Ming-Tai, 2024. "A parallel compact Marine Predators Algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 65-88.
    16. Thomas Wong & Mauricio Barahona, 2023. "Deep incremental learning models for financial temporal tabular datasets with distribution shifts," Papers 2303.07925, arXiv.org, revised Oct 2023.
    17. Townsend Peterson, A., 2007. "Why not WhyWhere: The need for more complex models of simpler environmental spaces," Ecological Modelling, Elsevier, vol. 203(3), pages 527-530.
    18. Si, Binghui & Tian, Zhichao & Jin, Xing & Zhou, Xin & Shi, Xing, 2019. "Ineffectiveness of optimization algorithms in building energy optimization and possible causes," Renewable Energy, Elsevier, vol. 134(C), pages 1295-1306.
    19. Fabio Caraffini & Giovanni Iacca, 2020. "The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-31, May.

    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:15:y:2022:i:23:p:8834-:d:981472. 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.