微電網逆變器之智慧型控制策略

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姓名

洪駿家(Chun-Chia Hung) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

微電網逆變器之智慧型控制策略
(Intelligent Control Strategy of Microgrid Inverter)

相關論文

★ 高頻高電流之雙向直流-直流轉換器設計

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摘要(中)

本論文旨在改善微電網在孤島模式時因負載變化而造成的電壓擾動與併網模式時的實虛功控制暫態響應，因此使用一具有模糊推理機制和線上學習能力的模糊類神經網路(Fuzzy Neural Network, FNN)來取代傳統比例積分(Proportional Integral, PI)控制器，該網路經常被應用在處理具有非線性和不確定性的控制系統。
本文模擬採用Solore公司所開發之電路模擬軟體PSIM (Power Simulation)來模擬一微電網之架構且分別操作於孤島模式和併網模式來驗證此演算法之可行性。在實作部分，硬體將以三相三線橋式轉換器作為逆變器之實體架構，將演算法寫入德州儀器(Texas Instruments, TI)之DSP TMS320F28335微控制器，最終驗證所提演算法之可靠性。

摘要(英)

This paper aims to improve the voltage disturbance caused by the load change at islanding mode and the transient response of the real and reactive power control at grid-connected mode in the microgrid. Therefore, the method proposed in this paper is to replace the traditional proportional integral(PI) controller with a fuzzy neural network(FNN) that has fuzzy inference mechanism and online learning capability. The network is often used to deal with nonlinear and uncertain control systems.
In this paper, the circuit simulation software PSIM (Power Simulation) developed by Solore is used to simulate the structure of the microgrid and operate at islanding mode and grid-connected mode respectively to verify the feasibility of this algorithm. For the practical implementation part, the hardware is based on three-phase three-wire bridge converter as the physical structure of the converter, and write the algorithm into the DSP TMS320F28335 micro-controller produced by Texas Instruments (TI) to verify the reliability of algorithm.

關鍵字(中)

★ 微電網
★ 孤島模式
★ 併網模式
★ 模糊類神經網路

關鍵字(英)

★ Microgrid
★ Islanding Mode
★ Grid-Connected Mode
★ Fuzzy Neural Network

論文目次

論文摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章緒論 1
1-1 研究背景與動機 1
1-2 文獻探討 2
1-3 論文大綱 4
第二章微電網之控制策略 5
2-1 控制策略之分類 5
2-1-1 具有通訊之控制策略 6
2-1-2 不具有通訊之控制策略 8
2-2 主從控制下之孤島模式與併網模式 10
2-3 微電網之相位檢測技術 13
2-3-1 零交越法(Zero-Crossing Method) 13
2-3-2 電網電壓濾波法 (Filtering of Grid Voltages) 14
2-3-3 鎖相迴路法(Phase Lock Loop, PLL) 15
2-4 併網規範 22
第三章模糊類神經網路 26
3-1 模糊理論 26
3-1-1 模糊集合(Fuzzy Sets) 27
3-1-2 模糊規則庫 29
3-1-3 模糊推論引擎 30
3-1-4 解模糊化 31
3-2 類神經網路 33
3-2-1 倒傳遞類神經網路 34
3-2-2 倒傳遞演算法 35
3-3 模糊類神經網路 37
3-3-1 模糊類神經網路線上學習法則 39
第四章系統架構與模擬結果 42
4-1 系統架構 42
4-2 控制策略 42
4-2-1 孤島模式 43
4-2-2 併網模式 44
4-3 正弦脈波寬度調變 45
4-4 孤島模式下之模擬波形 46
4-5 併網模式下之模擬波形 52
第五章實驗結果 55
5-1 系統硬體介紹 55
5-2 系統微控制器介紹 62
5-3 孤島模式下之波形 65
第六章結論與未來展望 70
6-1 結論 70
6-2 未來研究方向 70
參考文獻 72

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指導教授

陳正一廖益弘(Cheng-I Chen Yi-Hung Liao)

審核日期

2020-8-20

推文