應用於儲能系統之智慧型太陽光電功率平滑化控制

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姓名

趙若妤(Jo-Yu Chao) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

應用於儲能系統之智慧型太陽光電功率平滑化控制
(Intelligent PV Power Smoothing Control with Energy Storage System)

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摘要(中)

由於太陽光電系統易受照度及溫度等環境因素影響而造成瞬間能量變化大，若大量併入市電系統，將影響電網的可靠度與穩定度。因此，本論文提出以非對稱歸屬函數之機率模糊類神經網路為架構之智慧型控制應用於磷酸鋰鐵電池儲能系統，目的為減緩太陽光電輸出至電網的功率波動。於此控制策略中，太陽光電實際輸出功率與平滑後輸出功率之間的差值將由電池儲能系統提供。論文中將詳細介紹非對稱歸屬函數之機率模糊類神經網路之架構與線上學習法則，並證明其收斂性。除此之外，在電池能量管理方面利用庫倫積分法實現電池電量狀態的估測，以避免電池過度充放電。根據再生能源導入電網實功率波動之規範，本研究所提之非對稱歸屬函數之機率模糊類神經網路明顯減緩太陽光電輸出功率波動，以提高電網之可靠度與穩定度。此外，與其他平滑控制方法相比，本論文透過非對稱歸屬函數之機率模糊類神經網路之控制實現既符合規範並且使所需電池容量最小化之目的。最後，利用模擬與實驗結果驗證所提功率平滑控制應用於電池儲能系統在不同照度變化情況下之成效。

摘要(英)

As a major problem for integrating photovoltaic (PV) power plant to the grid, power fluctuations lead to poor power quality. A possible solution for regulating the intermittent output power of a PV power plant is to integrate a battery energy storage system (BESS). Therefore, an intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed to mitigate the fluctuation of PV output power directly fed to the grid. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. In addition, the state of charge (SOC) estimation using Coulomb counting method is adopted in the energy management of battery. According to the grid active power fluctuation limits set in this study, the proposed method is capable of mitigating the fluctuation of PV output power to improve reliability and stability of the grid. Furthermore, comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. Finally, the experimental results of various PV variation sceneries are realized to validate the effectiveness of the proposed intelligent PV power smoothing control.

關鍵字(中)

★ 太陽能發電廠
★ 電池儲能系統
★ 磷酸鋰鐵電池
★ 功率平滑化控制
★ 非對稱歸屬函數之機率模糊類神經網路
★ 電池電量狀態

關鍵字(英)

★ Photovoltaic (PV)
★ battery energy storage system (BESS)
★ LiFePO4 battery
★ power smoothing control
★ probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF)
★ state of charge (SOC)

論文目次

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 文獻回顧 3
1.3 本文貢獻 6
1.4 論文大綱 6
第二章磷酸鋰鐵電池儲能系統 8
2.1 簡介 8
2.2 鋰離子電池 8
2.2.1 鋰離子之電化學原理 8
2.2.2 磷酸鋰鐵電池之特性 11
2.3 電池電量狀態估測 11
2.3.1 開路電壓法 11
2.3.2 比重法 12
2.3.3 內阻法 12
2.3.4 庫倫積分法 13
2.4 雙級與單級儲能系統 13
2.5 太陽能發電廠 15
2.6 三相座標軸轉換 17
2.6.1 靜止坐標軸 19
2.6.2 同步旋轉座標軸 20
2.6.3 三相功率計算 21
2.7 市電角度估測法 22
2.7.1 線電壓軸轉換法 23
2.7.2 電壓濾波法 24
2.7.3 鎖相迴路法 24
2.8 變流器之實虛功控制與電流控制 25
2.9 硬體設備 27
2.9.1 磷酸鋰鐵電池 28
2.9.2 變流器 29
2.9.3 資料擷取卡 31
2.9.4 太陽能發電廠模擬器 32
第三章功率平滑化控制技術探討 33
3.1 簡介 33
3.2 功率波動 33
3.3 平均輸出法 34
3.4 移動平均法 34
3.5 一階低通濾波器 35
3.6 機率模糊類神經網路 36
3.6.1 機率模糊類神經網路架構 36
3.6.2 機率模糊類神經網路線上學習法則 39
第四章非對稱歸屬函數之機率模糊類神經網路 42
4.1 簡介 42
4.2 非對稱歸屬函數之機率模糊類神經網路架構 42
4.3 非對稱歸屬函數之機率模糊類神經網路線上學習法則 46
4.4 非對稱歸屬函數之機率模糊類神經網路收斂性分析 48
第五章單級智慧型磷酸鋰鐵電池儲能系統 51
5.1 系統簡介 51
5.2 功率平滑化控制策略 52
5.3 功率平滑化方法之比較 53
5.4 不同太陽光照度變化條件下功率平滑化之模擬 59
5.4.1 照度變化(700 W/m2-900 W/m2-1000 W/m2-900 W/m2) 63
5.4.2 照度變化(900 W/m2-1000 W/m2-900 W/m2-800 W/m2) 65
5.4.3 照度變化(700 W/m2-800 W/m2-700 W/m2-900 W/m2-1000 W/m2) 67
5.5 實作結果與討論 69
5.5.1 單級磷酸鋰鐵電池儲能系統測試 69
5.5.2 照度變化(700 W/m2-900 W/m2-1000 W/m2-900 W/m2) 72
5.5.3 照度變化(900 W/m2-1000 W/m2-900 W/m2-800 W/m2) 75
5.5.4 照度變化(700 W/m2-800 W/m2-700 W/m2-900 W/m2-1000 W/m2) 78
第六章結論與未來研究方向 82
6.1 結論 82
6.2 未來研究方向 83
參考文獻 84
作者簡歷 90

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2017-8-18

推文