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A Novel Quantum-Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive

Author

Listed:
  • Jamal Abd Ali

    (Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environments, University Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
    Ministry of Electricity, General Company of Electricity Production Middle Region, Baghdad 10001, Iraq)

  • Mahammad A Hannan

    (Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environments, University Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
    These authors contributed equally to this work.)

  • Azah Mohamed

    (Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environments, University Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
    These authors contributed equally to this work.)

Abstract

This paper presents a novel lightning search algorithm (LSA) using quantum mechanics theories to generate a quantum-inspired LSA (QLSA). The QLSA improves the searching of each step leader to obtain the best position for a projectile. To evaluate the reliability and efficiency of the proposed algorithm, the QLSA is tested using eighteen benchmark functions with various characteristics. The QLSA is applied to improve the design of the fuzzy logic controller (FLC) for controlling the speed response of the induction motor drive. The proposed algorithm avoids the exhaustive conventional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive input and output MFs are implemented in the fuzzy speed controller design to formulate the objective functions. Mean absolute error (MAE) of the rotor speed is the objective function of optimization controller. An optimal QLSA-based FLC (QLSAF) optimization controller is employed to tune and minimize the MAE, thereby improving the performance of the induction motor with the change in speed and mechanical load. To validate the performance of the developed controller, the results obtained with the QLSAF are compared to the results obtained with LSA, the backtracking search algorithm (BSA), the gravitational search algorithm (GSA), the particle swarm optimization (PSO) and the proportional integral derivative controllers (PID), respectively. Results show that the QLASF outperforms the other control methods in all of the tested cases in terms of damping capability and transient response under different mechanical loads and speeds.

Suggested Citation

  • Jamal Abd Ali & Mahammad A Hannan & Azah Mohamed, 2015. "A Novel Quantum-Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive," Energies, MDPI, vol. 8(11), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12358-13136:d:59014
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    References listed on IDEAS

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    1. Jinhyun Park & Houn Jeong & In Gyu Jang & Sung-Ho Hwang, 2015. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method," Energies, MDPI, vol. 8(8), pages 1-25, August.
    2. Ji, Bin & Yuan, Xiaohui & Chen, Zhihuan & Tian, Hao, 2014. "Improved gravitational search algorithm for unit commitment considering uncertainty of wind power," Energy, Elsevier, vol. 67(C), pages 52-62.
    3. Minh Quan Duong & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Kim Hung Le, 2015. "Improving Transient Stability in a Grid-Connected Squirrel-Cage Induction Generator Wind Turbine System Using a Fuzzy Logic Controller," Energies, MDPI, vol. 8(7), pages 1-22, June.
    4. Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
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    Citations

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    Cited by:

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    2. Dileep Kumar & Surya Deo Choudhary & Md Tabrez & Afida Ayob & Molla Shahadat Hossain Lipu, 2022. "Model Antiseptic Control Scheme to Torque Ripple Mitigation for DC-DC Converter-Based BLDC Motor Drives," Energies, MDPI, vol. 15(21), pages 1-24, October.
    3. Ammar Hussein Mutlag & Azah Mohamed & Hussain Shareef, 2016. "A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board," Energies, MDPI, vol. 9(3), pages 1-32, February.
    4. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    5. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    6. Reza Sirjani, 2017. "Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm," Sustainability, MDPI, vol. 9(9), pages 1-20, September.

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