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Intelligent-controlled doubly fed induction generator system using PFNN

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Abstract

An intelligent-controlled doubly fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous, and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system at different operating conditions. The network structure, online learning algorithm, and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results.

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Abbreviations

v abs , v bcs , v cas :

Three-phase stator line voltage

i as , i bs , i cs :

Three-phase stator current

i al , i bl , i cl :

Three-phase load current

i ar , i br , i cr :

Three-phase rotor current

i a , i b , i c :

Three-phase current of grid side converter

L a , L b , L c :

Three-phase inductor at grid side converter

v an , v bn , v cn :

Three-phase stator phase voltage

C :

DC-link capacitance

V :

Magnitude of stator phase voltage

V n :

Magnitude of phase A stator voltage

\( V_{n}^{*} \) :

Command magnitude of phase A stator voltage

V dc :

DC-link voltage

\( V_{\text{dc}}^{*} \) :

Command of DC-link voltage

d,q :

d-q axis

v ds , v qs :

Two-axis stator voltage

R s :

Stator resistance

λ ds , λ qs :

Two-axis stator flux linkage

i ds , i qs :

Two-axis stator current

i dr , i qr :

Two-axis rotor current

L s :

Stator inductance

L m :

Magnetizing inductance

P :

Pole numbers

θ e , ω e :

Electric angle and angular speed of stator phase voltage

θ r , ω r :

Rotor angle and angular speed

θ sl , ω sl :

Slip angle and angular speed

θ m , ω m :

Mechanical angle and angular speed

P s :

Real power of grid side converter

Q s :

Reactive power of grid side converter

v d , v q :

Two-axis voltage of grid side converter

\( i_{d} ,i_{d}^{*} \) :

d-axis current and command current of grid side converter

\( i_{q} ,i_{q}^{*} \) :

q-axis current and command current of grid side converter

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Acknowledgments

This work was supported in part by the Institute of Nuclear Energy Research of Taiwan, R.O.C. through its grant 1002001INER063.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Central University, Chungli, 320, Taiwan

    Faa-Jeng Lin & Zong-Han Lu

  2. Department of Electrical Engineering, National Ilan University, Ilan, 260, Taiwan

    Yi-Sheng Huang

  3. Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan, 335, Taiwan

    Kuang-Hsiung Tan

  4. Engineering Technology and Facility Operation Division, Institute of Nuclear Energy Research, Taoyuan, 335, Taiwan

    Yung-Ruei Chang

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  1. Faa-Jeng Lin
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  2. Yi-Sheng Huang
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  3. Kuang-Hsiung Tan
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  4. Zong-Han Lu
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Correspondence to Faa-Jeng Lin.

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Lin, FJ., Huang, YS., Tan, KH. et al. Intelligent-controlled doubly fed induction generator system using PFNN. Neural Comput & Applic 22, 1695–1712 (2013). https://doi.org/10.1007/s00521-012-0965-7

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  • DOI: https://doi.org/10.1007/s00521-012-0965-7

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