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Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

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  • Yuan, Xiaofang
  • Liu, Yuanming
  • Xiang, Yongzhong
  • Yan, Xinggang

Abstract

Bidirectional inductive power transfer (BIPT) system facilitates contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. Typically, this system is nonlinear high order system which includes nonlinear switch components and resonant networks, developing of accurate model is a challenging task. In this paper, a novel technique for parameter identification of a BIPT system is presented by using chaotic-enhanced fruit fly optimization algorithm (CFOA). The fruit fly optimization algorithm (FOA) is a new meta-heuristic technique based on the swarm behavior of the fruit fly. This paper proposes a novel CFOA, which employs chaotic sequence to enhance the global optimization capacity of original FOA. The parameter identification of the BIPT system is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured values. All the 11 parameters of this system (Lpi, LT, Lsi, Lso, CT, Cs, M, Rpi, RT, Rsi and Rso) can be identified simultaneously using measured input–output data. Simulations show that the proposed parameter identification technique is robust to measurements noise and variation of operation condition and thus it is suitable for practical application.

Suggested Citation

  • Yuan, Xiaofang & Liu, Yuanming & Xiang, Yongzhong & Yan, Xinggang, 2015. "Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 1267-1281.
  • Handle: RePEc:eee:apmaco:v:268:y:2015:i:c:p:1267-1281
    DOI: 10.1016/j.amc.2015.07.030
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    References listed on IDEAS

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    1. Yang, Dixiong & Li, Gang & Cheng, Gengdong, 2007. "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(4), pages 1366-1375.
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    Cited by:

    1. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    2. Fei Ye & Xin Yuan Lou & Lin Fu Sun, 2017. "An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-36, April.

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