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研究生： 曾梓渝
Tzu-Yu Tseng
論文名稱： 微電網虛擬同步發電機之智慧型控制
Intelligent Control of Virtual Synchronous Generator for Microgrid
指導教授： 林法正
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2021
畢業學年度： 109
語文別： 中文
論文頁數： 170
中文關鍵詞： 虛擬同步發電機 、下垂控制 、搖擺方程式 、虛擬慣量 、儲能系統 、太陽光發電系統 、派翠機率小波模糊類神經網路 、分散式能源
外文關鍵詞： virtual synchronous generator, droop control, swing equation, virtual inertia, storage system, photovoltaic, Petri probabilistic wavelet fuzzy neural network, distributed resources
相關次數： 點閱：24  下載：0 
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    • 本論文提出一種虛擬同步發電機的控制策略，利用虛擬慣量估測器來估測虛擬同步發電機的虛擬慣量，並減少在負載變化下分散式發電機的實功率輸出和頻率的振盪。此外，為了提高虛擬慣量估測器的性能與分散式發電機的實功率輸出和頻率的暫態響應，提出了一種線上訓練的派翠機率小波模糊神經網絡（PPWFNN）控制器來取代傳統的比例積分(PI)控制器。本文也介紹了派翠機率小波模糊神經網絡的結構和倒傳遞調整的線上學習算法。另外，也提出了下垂控制與低頻負載卸除的控制策略。微電網由儲能系統、太陽能（PV）系統和負載組成。低頻負載卸除是防止連續頻率下降和防止停電的主要措施。本研究為了克服傳統下垂控制在負載變化下的暫態響應緩慢與抗擾性差等缺點，也應用了線上訓練的派翠機率小波模糊神經網絡控制器來取代傳統的PI控制器。最後，根據模擬和實驗結果，使用所提出的虛擬慣量估測器和派翠機率小波模糊神經網絡控制器，可以實現在負載變化下提升實功率輸出的暫態響應、頻率響應和快速卸載的性能。

    In this thesis, a novel virtual inertia estimation methodology is proposed to estimate the suitable virtual inertia of the virtual synchronous generator (VSG) and to reduce the oscillations of the active power output and frequency of the distributed generator (DG) under load variation. Moreover, to improve the performance of the proposed virtual inertia estimator and the transient responses of the active power output and frequency of the DG, an online trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is proposed to replace the conventional proportional-integral (PI) controller. The network structure and the online learning algorithm using backpropagation (BP) of the proposed PPWFNN are represented in detail. Furthermore, a microgrid based on droop control with under-frequency load shedding (UFLS) algorithm is also researched in this study. The microgrid is composed of a storage system, a photovoltaic (PV) system and the load. The UFLS is the principal measure to prevent successive frequency declination and blackouts. In this study, to overcome the drawbacks of the traditional droop control such as slow transient response and poor disturbance rejection under load variation. The online trained PPWFNN controller is also proposed to replace the conventional PI controller in microgrid. Finally, according to the simulation and experimental results, superior performance of the active power output, frequency response and rapid load shedding under load variation can be achieved by using the proposed virtual inertia estimator and the intelligent PPWFNN controller.

    摘要 I Abstract II 誌謝 III 目錄 IV 圖目錄 VIII 表目錄 XV 第一章 緒 論 1 1.1 研究背景與動機 1 1.2 文獻回顧 3 1.3 論文大綱 8 1.4 本文貢獻 9 第二章 微電網規範與控制策略介紹 11 2.1 微電網規範 11 2.1.1 IEEE 929-2000規範 11 2.1.2 IEEE 1547-2003規範 12 2.1.3 電流諧波 13 2.1.4 電流諧波定義 13 2.1.5 諧波失真率公式及現行諧波管制標準 14 2.2 微電網控制策略 17 2.2.1 虛擬同步發電機策略之下垂控制方程式與曲線 17 2.2.2 虛擬同步發電機策略之下垂控制方程式設計 18 2.2.3 虛擬同步發電機 19 2.2.4 虛擬轉動慣量估測的限制器演算法 22 2.2.5 定功率控制[66] 24 2.2.6 電壓頻率控制 25 2.2.7 預同步控制策略 26 2.2.8 下垂控制策略之下垂控制方程式與曲線 30 2.2.9 下垂控制策略之下垂控制方程式設計 32 第三章 系統架構與控制策略 34 3.1 簡介 34 3.2 三相座標軸轉換 34 3.3 鎖相迴路 41 3.3.1 以同步旋轉座標軸實現的鎖相迴路 41 3.3.2 以靜止座標軸實現的鎖相迴路 42 3.3.3 以同步旋轉座標軸實現的固定頻率式鎖相迴路 43 3.4 實功率與虛功率的計算 46 3.5 低通濾波器 54 3.5.1一階低通濾波器 54 3.5.2二階低通濾波器 55 3.5.3移動平均法 56 3.6 虛擬同步發電機控制架構 57 3.6.1 虛擬同步發電機之控制策略 57 3.7 下垂控制策略與負載卸除方案 61 3.7.1 下垂控制策略 61 3.7.2 負載卸除方案 65 第四章 派翠機率小波模糊類神經網路 66 4.1 簡介 66 4.2 派翠機率小波模糊類神經網路架構 66 4.3 派翠機率小波模糊類神經網路線上學習法則 70 4.4 派翠機率小波模糊類神經網路收斂性分析 73 第五章 模擬結果 76 5.1 虛擬同步發電機策略的模擬結果 76 5.1.1 情境一：固定的虛擬轉動慣量模擬結果 77 5.1.2 情境二：負載為0.25kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之模擬結果 81 5.1.3 情境三：負載為0.5kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之模擬結果 85 5.2 下垂控制策略與負載卸除策略之模擬結果 89 5.2.1 情境一：負載變化為1kW-2kW的模擬結果 90 5.2.2 情境二：負載變化為1kW-3kW-2kW的模擬結果 94 5.2.3 情境三：負載變化為1kW-3.5kW-3kW的模擬結果 98 第六章 硬體與實驗結果 102 6.1 簡介 102 6.2 儲能系統硬體設備 103 6.2.1 儲能系統變流器 104 6.2.2 電阻負載之規劃 105 6.3 儲能系統週邊電路 106 6.3.1 交流電流回授電路 106 6.3.2 交流電壓回授電路 107 6.3.3 直流電壓回授電路 108 6.3.4 過電壓與過電流保護電路 109 6.3.5 開關互鎖電路 111 6.3.6 數位訊號處理器 114 6.3.7 DAC轉換電路 114 6.4 太陽能光電系統硬體設備 117 6.4.1 可程控直流電源供應器(具太陽能電池陣列模擬功能) 118 6.4.2 太陽能光電系統變流器 119 6.4.3 資料擷取卡 121 6.5 虛擬同步發電機策略之實驗結果 122 6.5.1 情境一：固定的虛擬轉動慣量實驗結果 122 6.5.2 情境二：負載為0.25kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之實驗結果 127 6.5.3 情境三：負載為0.5kW-2kW-1kW，PI、FNN與PPWFNN控制器估測虛擬轉動慣量之實驗結果 131 第七章 結論與未來展望 135 7.1 結論 135 7.2 未來展望 136 參考文獻 137 作者簡歷 146

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