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Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm

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  • Ali, Ehab S.

Abstract

This paper proposes the speed control of IM (Induction Motor) fed by wind turbine using ICA (Imperialist Competitive Algorithm). The wind turbine plays as a prime mover to a connected DC (Direct Current) generator. PWM (Pulse Width Modulation) is used to get three phase AC (Alternating Current) voltage from the output of DC generator. The proposed design problem of speed controller is established as an optimization problem. ICA is adopted to search for optimal controller parameters by minimizing the time domain objective function. The behavior of the proposed ICA has been estimated with the behavior of the conventional ZN (Ziegler–Nichols) and GA (Genetic Algorithm) in order to prove the superiority of the proposed ICA in tuning PI (Proportional plus Integral) controller. Also, the behavior of the proposed controller has been tested over a wide range of operating conditions. Simulation results confirm on the better behavior of the optimized PI controller based on ICA compared with other algorithms.

Suggested Citation

  • Ali, Ehab S., 2015. "Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm," Energy, Elsevier, vol. 89(C), pages 593-600.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:593-600
    DOI: 10.1016/j.energy.2015.06.011
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    References listed on IDEAS

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    1. Mohammadi-ivatloo, Behnam & Rabiee, Abbas & Soroudi, Alireza & Ehsan, Mehdi, 2012. "Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch," Energy, Elsevier, vol. 44(1), pages 228-240.
    2. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
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    2. Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
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