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

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Electrical Engineering > Electronic Thesis & Dissertation > Item 987654321/65749

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65749

Title:	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
Authors:	李軒宇;Lee,Hsuan-yu
Contributors:	電機工程學系
Keywords:	太陽能;低電壓穿越;智慧型;電網
Date:	2014-07-25
Issue Date:	2014-10-15 17:09:27 (UTC+8)
Publisher:	國立中央大學
Abstract:	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.
Appears in Collections:	[Graduate Institute of Electrical Engineering] Electronic Thesis & Dissertation

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