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Investigation and analysis of high performance green energy induction motor drive with intelligent estimator

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  • Chitra, A.
  • Himavathi, S.

Abstract

This paper attempts to enhance the performance of a green energy induction motor drive. The electronic power converters become indispensable part of the renewable energy systems (RES). The solar photovoltaic (PV) system is efficiently operated with artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm. The inverter topologies for the green drive scheme are analyzed. To improve the drive performance a reduced switch multilevel inverter (RSMLI) is employed. As indirect field oriented control (IFOC) is used, the drive demands on-line estimation of rotor resistance. A neural learning model reference adaptive scheme (NL-MRAS) based rotor resistance estimator is found to exhibit good dynamic performance. This work also investigates the performance of the green drive with an intelligent estimator. The performance enhancement of the green energy drive obtained by ANN based MPPT for the PV system, a reduced switch MLI and an intelligent estimator is presented.

Suggested Citation

  • Chitra, A. & Himavathi, S., 2016. "Investigation and analysis of high performance green energy induction motor drive with intelligent estimator," Renewable Energy, Elsevier, vol. 87(P2), pages 965-976.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:965-976
    DOI: 10.1016/j.renene.2015.07.084
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    References listed on IDEAS

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    1. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
    2. Salam, Zainal & Ahmed, Jubaer & Merugu, Benny S., 2013. "The application of soft computing methods for MPPT of PV system: A technological and status review," Applied Energy, Elsevier, vol. 107(C), pages 135-148.
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    Cited by:

    1. Ahmed A. Zaki Diab & Mohammed A. Elsawy & Kotin A. Denis & Salem Alkhalaf & Ziad M. Ali, 2022. "Artificial Neural Based Speed and Flux Estimators for Induction Machine Drives with Matlab/Simulink," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    2. Tuan Pham Van & Dung Vo Tien & Zbigniew Leonowicz & Michal Jasinski & Tomasz Sikorski & Prasun Chakrabarti, 2020. "Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive," Energies, MDPI, vol. 13(18), pages 1-16, September.

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