Intelligent-controlled doubly fed induction generator system using PFNN |
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Authors: | Faa-Jeng Lin Yi-Sheng Huang Kuang-Hsiung Tan Zong-Han Lu Yung-Ruei Chang |
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Affiliation: | 1. Department of Electrical Engineering, National Central University, Chungli, 320, Taiwan 2. Department of Electrical Engineering, National Ilan University, Ilan, 260, Taiwan 3. Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan, 335, Taiwan 4. Engineering Technology and Facility Operation Division, Institute of Nuclear Energy Research, Taoyuan, 335, Taiwan
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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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