以OPAL-RT硬體迴圈實現微電網之智慧型控制

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蕭果登(GUO-DENG XIAO) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以OPAL-RT硬體迴圈實現微電網之智慧型控制
(Intelligent Control of Microgrid with OPAL-RT Hardware in the Loop)

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至系統瀏覽論文 (2025-8-1以後開放)

摘要(中)

本論文利用OPAL-RT即時模擬系統與硬體迴圈(Hardware In the Loop, HIL)功能建置一以七美島電力系統為原型之微電網。本論文的微電網系統控制方法採用主、從控制法則，本論文由一儲能系統、太陽光發電系統、風力發電系統與三條負載饋線所組成。其中以儲能系統當作微電網控制主機(Master)，而太陽光發電系統及風力發電系統則定位為從屬(Slave)部分。此外，為了改善在併網時的實虛功率控制、併網轉孤島模式時的模式切換以及孤島狀態下時因日照或負載變化所造成之暫態響應，本論文除了以模糊類神經網路(Fuzzy Neural Network, FNN)取代傳統比例積分控制器外，也提出一線上訓練的非對稱歸屬函數之小波派翠模糊類神經網路(Wavelet Petri Fuzzy Neural Network – Asymmetric Membership Function, WPFNN-AMF)將其取代並將三者的效益做對比。本文將詳細介紹WPFNN-AMF的網路架構與線上學習法則。最後，以HIL實驗結果驗證使用FNN、WPFNN-AMF等智慧型控制器在不同操作模式下之有效性與可行性。

摘要(英)

This paper uses the OPAL-RT real-time simulation system and hardware in the loop (HIL) function to build a microgrid based on the Cimei island power system. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system, a wind turbine system and three load feeders. Among them, the energy storage system is regarded as a master unit, and the photovoltaic (PV) system and the wind turbine generator system are positioned as slave units. Moreover, in order to improve the control of active and the reactive power in grid-connected mode, the transient response of the switching during the grid-connected mode to islanding mode, and the transient response which caused by irradiance or load changes in the island mode, two online trained intelligent controllers are proposed to replace the conventional proportional-integral (PI) controller in the storage system, one is fuzzy neural network (FNN), and the other is wavelet petri fuzzy neural network with an asymmetric membership function (WPFNN-AMF). This paper will introduce the WPFNN-AMF network architecture and online learning rules in detail. Finally, the HIL experiment results are used to verify the effectiveness and feasibility of using FNN, WPFNN-AMF intelligent controllers in different operating modes.

關鍵字(中)

★ 微電網
★ 主從控制
★ OPAL-RT即時模擬
★ 硬體迴圈
★ 小波派翠模糊類神經網路
★ 非對稱歸屬函數

關鍵字(英)

★ Microgrid
★ master-slave control
★ OPAL-RT real-time simulation
★ hardware in the loop
★ wavelet petri fuzzy neural network
★ asymmetric membership function

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 XII
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 6
1.4 本文貢獻 7
第二章微電網與分散式電源介紹 8
2.1 簡介 8
2.2 微電網控制策略 8
2.2.1 定功率控制(PQ control) 8
2.2.2 電壓頻率控制(V/F control) 8
2.2.3 主從控制 9
2.2.4 分級控制 10
2.3 微電網規範 12
2.3.1 IEEE 1547-2003規範 12
2.4 分散式電源介紹 13
2.4.1 太陽能電池特性 13
2.4.2 風力發電系統 17
2.4.3 儲能系統 18
第三章系統架構與控制策略 21
3.1 簡介 21
3.2 三相座標軸轉換 21
3.3 鎖相迴路 23
3.4 變流器之實、虛功控制與電流控制 24
3.5 電力系統架構與控制 25
3.5.1 七美島電力系統介紹 25
3.5.2 系統架構與控制策略 27
3.5.3 主控制策略 29
3.5.4 從控制策略 31
第四章非對稱歸屬函數之小波派翠模糊類神經網路 35
4.1 簡介 35
4.2 非對稱歸屬函數之小波派翠模糊類神經網路架構 35
4.3 非對稱歸屬函數之小波派翠模糊類神經網路之線上學習法則 40
4.4 非對稱歸屬函數之小波派翠模糊類神經網路收斂性分析 43
第五章模擬結果 46
5.1 模擬情境 46
5.2 情境一之模擬結果 47
5.3 情境二之模擬結果 57
5.4 情境三之模擬結果 67
5.5 情境四之模擬結果 75
第六章硬體迴圈與實驗結果 83
6.1 簡介 83
6.2 即時模擬系統 84
6.2.1 即時模擬介紹 84
6.2.2 OP4510硬體 87
6.2.3 軟體介面 RT-LAB 89
6.2.4 模型分割 92
6.2.4.1 OpComm 92
6.2.4.2 模型分割及命名 92
6.2.4.3 Artemis Stubline 94
6.2.5 硬體迴圈規劃 95
6.3 數位訊號處理器 97
6.5 實驗結果 99
6.5.1 情境一實驗結果 100
6.5.2 情境二實驗結果 110
6.5.3 情境三實驗結果 119
6.5.4 情境四實驗結果 128
第七章結論與未來展望 137
7.1 結論 137
7.2 未來展望 138
參考文獻 139
作者簡歷 147

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指導教授

林法正(FAA-JENG LIN)

審核日期

2020-8-13

推文