微電網虛擬同步發電機之智慧型控制

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曾梓渝(Tzu-Yu Tseng) 查詢紙本館藏

畢業系所

電機工程學系

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)

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摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

★ virtual synchronous generator
★ droop control
★ swing equation
★ virtual inertia
★ storage system
★ photovoltaic
★ Petri probabilistic wavelet fuzzy neural network
★ distributed resources

論文目次

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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指導教授

林法正

審核日期

2021-8-19

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