本文主要為太陽能與柴油-風力混合發電系統之研製，此系統包括太陽能發電系統、風力發電系統、柴油引擎發電系統、靜態同步補償器以及智慧型控制器。本文利用MATLAB/Simulink 來建立太陽能與柴油-風力混合發電系統之動態模型，並進行動態模擬分析，並且應用一靜態同步補償器提供系統無效功率，來調整混合發電系統的電壓。為了使混合發電系統皆可操作在最大功率點及系統實功控制達到一個快速又穩定的響應，本文所提出之智慧型控制器，包含徑向基底類神經網路(Radial Basis Function Network, RBFN)與遞迴類神經網路(Elman Neural Network, ENN)，將其應用於風力發電系統與太陽能發電系統之最大功率追蹤技術。其中風力發電系統之葉片旋角控制器是利用遞迴類神經網路控制器，由控制器輸出的旋角，輸入到風力渦輪機系統中，來控制風力渦輪機的輸出功率及發電機輸出功率，以達到最大功\率追蹤。而太陽能發電系統則利用徑向基底類神經網路控制器，由控制器輸出信號來控制直流/直流昇壓器，以達到最大功率追蹤。

A solar and diesel-wind hybrid power control systems is proposed in the thesis. The system consists of solar power, wind power, diesel-engine, a static synchronous compensator and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the solar and diesel-wind hybrid power system. A static synchronous compensator was used to supply reactive power and regulate the voltage of the hybrid system. To achieve a fast and stable response for the real power control, an intelligent controller was proposed, which consists of the Radial Basis Function Network (RBFN) and the Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle control of wind power uses ENN controller, and the output is fed to the wind turbine to achieve the MPPT. The solar system uses RBFN, and the output signal is used to control the DC / DC boost converters to achieve the MPPT.

中文摘要I
AbstractII
目錄III
圖目錄VII
表目錄XII
第一章 緒論
1-1 研究背景1
1-2 研究動機2
1-3 相關文獻回顧3
1-4 本論文的貢獻4
1-5 論文架構4
第二章 混合發電系統之模型與原理
2-1 風力發電系統介紹7
2-1-1 風能簡介7
2-1-2 風力機發電原理7
2-1-3 風力發電系統控制原理10
2-1-4 感應發電機之基本原理11
2-1-5 感應發電機之數學模型分析12
2-1-6 風力發電系統之工作模式16
2-2 太陽能發電系統介紹17
2-2-1 太陽能簡介17
2-2-2 太陽能電池特性17
2-2-3 太陽能模組21
2-2-4 升壓型直流/直流升壓器模型23
2-2-5 三相反流器24
2-2-6 電流控制器26
2-2-7 太陽能發電系統之工作模式28
2-3 直流/交流轉換器之數學模型29
2-3-1 直流/交流轉換器之d-q 軸數學模型29
2-3-2 直流/交流轉換器之a-b-c 軸數學模型30
2-4 柴油系統動態模型30
2-4-1 柴油系統簡介30
2-4-2 同步發電機模型31
2-4-3 柴油引擎調速機模型33
2-4-4 激磁系統模型34
第三章 靜態同步補償器之原理與分析
3-1 前言35
3-2 靜態同步補償器原理與特性分析35
3-3 靜態同步補償器之數學模型分析38
3-4 靜態同步補償器之控制器設計41
第四章 類神經網路之理論與應用
4-1 前言42
4-2 類神經網路控制架構42
4-3 徑向基底類神經網路原理與架構45
4-3-1 徑向基底類神經網路原理45
4-3-2 徑向基底類神經網路架構46
4-4 Elman 類神經網路原理與架構49
4-4-1 Elman 類神經網路原理49
4-4-2 Elman 類神經網路架構50
第五章 混合發電之最大功率追蹤控制
5-1 前言54
5-2 風力發電系統之最大功率追蹤設計原理54
5-2-1 旋角控制之設計原理54
5-2-2 比例-積分之旋角控制55
5-2-3 Elman 類神經網路於旋角控制之設計56
5-3 太陽能發電系統之最大功率追蹤設計原理57
5-3-1 太陽能發電系統之最大功率追蹤控制57
5-3-2 徑向基底類神經網路於最大功率追蹤之設計60
第六章 混合發電系統之模擬結果
6-1 前言62
6-2 混合發電系統架構62
6-3 模擬與驗證63
6-3-1 市電併聯型風能-柴油引擎混合發電系統63
6-3-2 市電併聯型太陽能-柴油引擎混合發電系統66
6-3-3 市電併聯型風能-太陽能混合發電系統69
6-3-4 獨立型太陽能-風能-柴油引擎混合發電系統70
6-3-5 市電併聯型太陽能-風能-柴油引擎混合發電系統73
6-3-6 獨立型混合發電系統最大功率追蹤之測試比較75
6-4 穩態模擬分析80
6-4-1 太陽照度變化影響分析80
6-4-2 風速變化影響分析83
6-4-3 負載變化下併聯靜態同步補償器之模擬分析87
6-5 事故模擬分析90
6-5-1 三相短路故障分析90
6-5-2 負載切換分析92
6-5-3 太陽能發電系統事故跳脫分析94
6-5-4 風力發電系統事故跳脫分析96
第七章 結論與未來研究方向
7-1 結論99
7-2 未來研究方向100
參考文獻101

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