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 987654321/65749
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 73032/73032 (100%)
造访人次 : 23113184      在线人数 : 385
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/65749


    题名: 具低電壓穿越能力之單級智慧型太陽能光電系統;An Single Stage Intelligent PV System with LVRT
    作者: 李軒宇;Lee,Hsuan-yu
    贡献者: 電機工程學系
    关键词: 太陽能;低電壓穿越;智慧型;電網
    日期: 2014-07-25
    上传时间: 2014-10-15 17:09:27 (UTC+8)
    出版者: 國立中央大學
    摘要: 本論文提出一種併網型單級三相太陽光電系統於配電系統三相故障期間使用二維遞迴式模糊小腦模型類神經網路之實虛功控制法,其中配電三相故障為相對地故障或相對相故障。太陽能光電系統利用單級三相電流控制電壓源之變流器完成最大功率點追蹤法以及低電壓穿透能力之功能,其中最大功率追蹤法採用增量電導法。配電系統三相故障時,控制器以符合E.ON低電壓穿透規範的虛功電流比例補償,調整注入市電端之虛功量,使太陽光電系統產生之實功與注入市電之實功維持平衡以及保持穩定,並限制變流器輸出電流之最大值。本論文首先利用三相電壓大小之最小值來判斷故障電壓之大小,並且提出一種以正序電壓成分之大小判斷故障電壓大小之方法。本論文所提出之智慧型控制器二維遞迴式模糊小腦模型類神經網路將在論文內詳細介紹架構中各層之函數以及藉由最高陡降法所推導之線上學習法則,並且使用李亞普諾夫函數證明其收斂性。最後利用實驗成果來驗證所提出智慧型控制器應用於併網型單級三相太陽光電系統之實虛功控制在配電系統三相故障時之成效,並與比例積分控制器來做比較。;A new active and reactive power control scheme using two-dimensional recurrent fuzzy cerebellar model articulation neural network (2D-RFCMANN) for a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults is proposed in this study. The presented PV system utilizes a single-stage three-phase current-controlled voltage-source inverter to achieve the maximum power point tracking (MPPT) control of the PV panel with the function of low voltage ride through (LVRT).Thus, a formula based on positive sequence voltage for evaluating the percentage of voltage sag is derived to determine the ratio of the injected reactive current to satisfy the LVRT regulations. Moreover, an incremental conductance (IC) method is adopted for the MPPT control. Furthermore, the constraint of the active and reactive power command of the control scheme is according to the ratio of the reactive current in order to meet the LVRT regulations. To reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. In addition, the recurrent network is embedded in the first layer of the 2D-RFCMANN and a Gaussian basis function is used to model the hypercube structure. The online learning laws of 2D-RFCMANN are derived according to gradient descent method. Additionally, specific learning-rate coefficients for network parameters to assure the convergence of the tracking error are derived using Lyapunov function. Finally, some experimental tests are realized to validate the effectiveness of the proposed control scheme.
    显示于类别:[電機工程研究所] 博碩士論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML333检视/开启


    在NCUIR中所有的数据项都受到原著作权保护.

    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 ©   - 回馈  - 隱私權政策聲明