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Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

Author

Listed:
  • Martin Ćalasan

    (Faculty of Electrical Engineering, University of Montenegro, Dzordza Vasingtona, 81000 Podgorica, Montenegro)

  • Mihailo Micev

    (Faculty of Electrical Engineering, University of Montenegro, Dzordza Vasingtona, 81000 Podgorica, Montenegro)

  • Ziad M. Ali

    (Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Sahary City 81542, Egypt)

  • Ahmed F. Zobaa

    (Electronic and Computer Engineering Department, Brunel University London, Uxbridge UB8 3PH, UK)

  • Shady H. E. Abdel Aleem

    (Mathematical, Physical and Engineering Sciences Department, 15th of May Higher Institute of Engineering, Cairo 11731, Egypt)

Abstract

This paper presents the usage of the hybrid simulated annealing—evaporation rate water cycle algorithm (SA-ERWCA) for induction machine equivalent circuit parameter estimation. The proposed algorithm is applied to nameplate data, measured data found in the literature, and data measured experimentally on a laboratory three-phase induction machine operating as an induction motor and as an induction generator. Furthermore, the proposed method is applied to both single-cage and double-cage equivalent circuit models. The accuracy and applicability of the proposed SA-ERWCA are intensively investigated, comparing the machine output characteristics determined by using SA-ERWCA parameters with corresponding characteristics obtained by using parameters determined using known methods from the literature. Also, the comparison of the SA-ERWCA with classic ERWCA and other algorithms used in the literature for induction machine parameter estimation is presented. The obtained results show that the proposed algorithm is a very effective and accurate method for induction machine parameter estimation. Furthermore, it is shown that the SA-ERWCA has the best convergence characteristics compared to other algorithms for induction machine parameter estimation in the literature.

Suggested Citation

  • Martin Ćalasan & Mihailo Micev & Ziad M. Ali & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2020. "Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:1024-:d:375121
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    References listed on IDEAS

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    1. Jing Tang & Yongheng Yang & Frede Blaabjerg & Jie Chen & Lijun Diao & Zhigang Liu, 2018. "Parameter Identification of Inverter-Fed Induction Motors: A Review," Energies, MDPI, vol. 11(9), pages 1-21, August.
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    Cited by:

    1. Faisal Altaf & Ching-Lung Chang & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Chi-Min Shu & Ahmad H. Milyani, 2022. "Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle," Mathematics, MDPI, vol. 10(6), pages 1-20, March.
    2. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).

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