中大機構典藏-NCU Institutional Repository-提供博碩士論文、考古題、期刊論文、研究計畫等下載:Item 987654321/44722
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 80990/80990 (100%)
Visitors : 41674948      Online Users : 1484
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/44722


    Title: 智慧型控制雙饋式感應風力發電系統之研製;Design and Implementation of DFIG Based Wind Generator System Using Intelligent Control
    Authors: 呂宗翰;Zong-Han Lu
    Contributors: 電機工程研究所
    Keywords: 雙饋式感應發電機;Doubly-fed induction generator;FOC
    Date: 2010-07-27
    Issue Date: 2010-12-09 13:53:44 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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.
    Appears in Collections:[Graduate Institute of Electrical Engineering] Electronic Thesis & Dissertation

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML808View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明