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Dynamic control for permanent magnet synchronous generator system using novel modified recurrent wavelet neural network

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Abstract

Because permanent magnet synchronous generator (PMSG) system driven by permanent magnet synchronous motor (PMSM) based on wind turbine emulator (WTE) is a nonlinear and time-varying system with high complication, an accurate dynamic model of the PMSG system directly driven by WTE is difficult to establish for the linear controller design. In order to conquer this difficulty and improve the robustness of dynamic system, the PMSG system controlled by the online-tuned parameters of the novel modified recurrent wavelet neural network (NN)-controlled system is proposed to control output voltages and powers of controllable rectifier and inverter in this study. First, a closed-loop PMSM-driven system based on WTE is designed for driving the PMSG system to generate output power. Second, the rotor speeds of the PMSG, the voltages, and currents of the two power converters are detected simultaneously to yield maximum power output. In addition, two sets of the online-tuned parameters of the modified recurrent wavelet NN controllers in the controllable rectifier and inverter are developed for the voltage-regulating controllers in order to improve output performance. Finally, some experimental results are verified to show the effectiveness of the proposed novel modified recurrent wavelet NN controller for the power output of the PMSG system driven by WTE.

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Authors and Affiliations

  1. Department of Electrical Engineering, National United University, No. 1, Lienda, Kung-Jing Li, Miaoli , 36003, Taiwan

    Chih-Hong Lin

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  1. Chih-Hong Lin
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Correspondence to Chih-Hong Lin.

Additional information

1. The modified recurrent wavelet NN controlled output power of the PMSG system.

2. A WTE is designed to generate the maximum power for the PMSG system.

3. Two sets online training modified recurrent wavelet NNs are developed.

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Lin, CH. Dynamic control for permanent magnet synchronous generator system using novel modified recurrent wavelet neural network. Nonlinear Dyn 77, 1261–1284 (2014). https://doi.org/10.1007/s11071-014-1376-3

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  • DOI: https://doi.org/10.1007/s11071-014-1376-3

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