結合虛擬慣量併網型微電網之智慧型控制

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石承民(CHENG-MING SHIH) 查詢紙本館藏

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電機工程學系

論文名稱

結合虛擬慣量併網型微電網之智慧型控制
(Intelligent Control of Grid-Connected Microgrid with Virtual Inertia)

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摘要(中)

本論文提出一種併網型微電網結合虛擬慣量利用主、從控制法則克服一般傳統分散式電源基於功率開關元件變流器之缺點，例如缺乏傳統發電機的慣量特性與獨立電網形成能力。本論文的微電網系統控制方法採用主、從控制法則，並且由一儲能系統、太陽光發電系統與一個三相可變電阻負載所組成。其中以儲能系統當作微電網控制主機(Master)，而太陽光發電系統則定位為從屬(Slave)部分。此外，為了改善儲能系統在併網模式時的虛功控制以及微電網在併網與孤島模式之間切換的暫態響應，本論文提出一線上訓練的遞迴式機率小波模糊類神經網路(Recurrent Probabilistic Wavelet Fuzzy Neural Network, RPWFNN)取代傳統比例積分控制器。此外，當微電網在孤島模式運轉時，負載的變化將會造成微電網電壓嚴重地波動，因此本文所提出的RPWFNN也可用來改善因負載變動所造成的微電網電壓波動。本文將詳細介紹RPWFNN的網路架構與線上學習法則。最後，以實驗結果驗證使用RPWFNN之結合虛擬慣量併網型微電網在不同操作模式下之有效性與可行性。

摘要(英)

A microgrid with virtual inertia using master-slave control is proposed in this study to overcome the drawbacks of traditional inverter-based distributed generators such as lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit respectively in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, the excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.

關鍵字(中)

★ 微電網
★ 虛擬慣量
★ 主從控制
★ 機率類神經網路
★ 小波模糊類神經網路
★ 遞迴式類神經網路

關鍵字(英)

★ Microgrid
★ virtual inertia
★ master-slave control
★ probabilistic neural network
★ wavelet fuzzy neural network
★ recurrent neural network

論文目次

摘要 I
Abstract II
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 文獻回顧 3
1.3 論文大綱 7
1.4 本文貢獻 8
第二章微電網規範與控制策略介紹 9
2.1 微電網規範 9
2.1.1 IEEE 929-2000規範 9
2.1.2 IEEE 1547-2003規範 10
2.2 微電網控制策略 11
2.2.1 定功率控制 11
2.2.2 電壓頻率控制 12
2.2.3 下降控制 13
2.2.4 虛擬同步發電機 14
2.2.5 主從控制 16
2.2.6 分級控制 17
第三章系統架構與控制策略 20
3.1 簡介 20
3.2 三相座標軸轉換 20
3.3 鎖相迴路 21
3.4 主從控制加入虛擬慣量之架構與控制策略 23
3.4.1 主控制策略與虛擬慣量 24
3.4.2 從控制策略 28
3.4.3 預同步控制策略 29
第四章遞迴式機率小波模糊類神經網路 32
4.1 簡介 32
4.2 遞迴式機率小波模糊類神經網路架構 32
4.3 遞迴式機率小波模糊類神經網路線上學習法則 36
4.4 遞迴式機率小波模糊類神經網路收斂性分析 39
第五章模擬結果 42
5.1 模擬結果 42
5.1.1 併網模式模擬結果 42
5.1.2 併網轉孤島模式模擬結果 47
5.1.3 孤島模式模擬結果 52
5.1.4 孤島轉併網模式模擬結果 57
第六章硬體與實驗結果 59
6.1 簡介 59
6.2 儲能系統介紹 59
6.2.1 磷酸鋰鐵電池 59
6.2.2 電池保護裝置 61
6.2.3 電池平衡裝置 62
6.3 儲能系統硬體設備 64
6.3.1 儲能系統變流器 64
6.3.2 電阻負載之規劃 66
6.4 儲能系統周邊電路 68
6.4.1 交流電流回授電路 68
6.4.2 交流電壓回授電路 69
6.4.3 直流電壓回授電路 70
6.4.4 保護電路 70
6.4.5 開關互鎖電路 71
6.4.6 數位訊號處理器 71
6.5 太陽能光電系統硬體設備 74
6.5.1 可程控直流電源供應器(具太陽能電池陣列模擬功能) 74
6.5.2 太陽能光電系統變流器 77
6.5.3 資料擷取卡 79
6.6 實驗結果 80
6.6.1 併網模式實驗結果 80
6.6.2 併網轉孤島模式實驗結果 86
6.6.3 孤島模式實驗結果 92
6.6.4 孤島轉併網模式實驗結果 98
第七章結論與未來展望 103
7.1 結論 103
7.2 未來展望 104
參考文獻 105
作者簡歷 112

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2019-7-31

推文