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Gravitational search algorithm based optimization technique for enhancing the performance of self excited induction generator

Author

Listed:
  • Swati Paliwal

    (Amity University Uttar Pradesh)

  • Sanjay Kumar Sinha

    (Amity University Uttar Pradesh)

  • Yogesh Kumar Chauhan

    (Kamla Nehru Institute of Technology)

Abstract

In wind based micro generation schemes, 3-phase self excited induction generators are prominently used in order to fulfil single phase load requirement. Hence in this context, this paper presents a 3-phase, 5.5 kW, 415 V, 50 Hz short shunt self excited induction generator for improving the voltage regulation and optimum performance of induction machine by using heuristic approach named as gravitational search algorithm. It is used in order to get the optimum capacitance values at specified speed for optimized voltage regulation and performance characteristics in terms of root mean square error and mean absolute error and mean square error. This optimization technique works on Newton law of gravity and it provides average best results for validating the performance in order to enhance machine parameters used in wind energy conversion system.

Suggested Citation

  • Swati Paliwal & Sanjay Kumar Sinha & Yogesh Kumar Chauhan, 2019. "Gravitational search algorithm based optimization technique for enhancing the performance of self excited induction generator," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1082-1090, October.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:5:d:10.1007_s13198-019-00838-1
    DOI: 10.1007/s13198-019-00838-1
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    References listed on IDEAS

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    1. 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.
    2. Chen, Diyi & Liu, Si & Ma, Xiaoyi, 2013. "Modeling, nonlinear dynamical analysis of a novel power system with random wind power and it's control," Energy, Elsevier, vol. 53(C), pages 139-146.
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