本論文以比例積分微分型類神經網路與機率型模糊類神經網路智慧型控制器實現獨立型雙饋式感應發電系統之控制。此系統可應用於獨立供電系統，或當市電發生故障時作為緊急發電系統之用途。此系統在次同步、同步以及超同步的情況以及負載變動下皆能夠產生穩定之三相220V的電壓以及60Hz的頻率。本論文利用轉子側轉換器磁場導向控制，可使雙饋式感應發電機在轉速變動下皆能夠產生固定的電壓大小和頻率；利用定子側轉換器磁場導向控制，以維持直流鏈電壓的穩定。論文中，亦詳細推導比例積分微分型類神經網路與機率型模糊類神經網路控制器之網路架構與線上學習法則，並應用於雙饋式感應發電系統之轉子側及定子側轉換器，使其達到更好的暫態響應與控制效能。另一方面，本論文亦採用PSIM軟體模擬雙饋式感應發電系統之可行性，最後透過實驗結果來驗證所提控制方法之有效性。An intelligent control doubly-fed induction generator (DFIG) system using proportional-integral-derivative neural network (PIDNN) and probabilistic fuzzy neural network (PFNN) is proposed in this study. For stand-alone applications, this system can be applied as a stand-alone power supply system or as an emergency power system when the electricity grid fails for all sub-synchronous, synchronous and super-synchronous conditions. The rotor side converter is controlled using field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the stator side converter, which is also controlled using field-oriented control, is implemented to maintain the magnitude of the DC-link voltage. Furthermore, the intelligent PIDNN controller and PFNN controller is proposed for both the rotor and stator side converters to improve the transient and steady-state responses of the DFIG system for different operating conditions. Both the network structure and on-line learning algorithm are introduced in detail. In addition, some simulated results are given to verify the design of the DFIG system. Finally, the feasibility of the proposed control scheme is verified through experimentation.
