具智慧型太陽光電功率平滑化控制之微電網電能管理系統

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姓名

郭哲男(Che-Nan Kuo) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

具智慧型太陽光電功率平滑化控制之微電網電能管理系統
(Intelligent PV Power Smoothing Control for Microgrid Energy Management System)

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摘要(中)

太陽光電系統受太陽光照度及環境溫度等因素影響，輸出功率具有高間歇性。由於功率瞬間變化量大，若大量併入電力系統，將影響市電的電力品質。因此，本論文提出以遞迴式機率小波模糊類神經網路為架構之智慧型控制，應用於微電網電能管理系統，目的為減緩太陽光電輸出至電網的功率波動，其原理為利用電池儲能系統，來彌補太陽光電實際輸出功率與平滑化後輸出功率之間的差值。本論文之電能管理系統使用聯齊科技的數據閘道器Atto做為硬體，並整合中興電工的微電網系統，Atto每分鐘收集微電網電力資訊儲存並上傳雲端，併網時，於Atto內執行太陽光電功率平滑化演算法，控制電池儲能系統的充放電來平滑化市電功率波動，市電發生異常切換至孤島時，太陽光電系統與電池儲能系統將提供穩定的交流電壓，使微電網維持正常運作。本論文將詳細介紹遞迴式機率小波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，於功率平滑化控制中，電池會頻繁地充放電，本論文提出基於模糊邏輯之減少電池淺充淺放次數控制法，降低電池的充放電次數使電池壽命延長，並以庫倫積分法估測電池電量狀態。根據再生能源導入電網實功率波動之規範，本研究所提之遞迴式機率小波模糊類神經網路不僅能符合規範，並且與其他平滑化控制法相比所需電池容量最小。最後，利用模擬與實驗結果驗證所提出之功率平滑化控制，應用於微電網電能管理系統且在不同照度變化情況下之成效。

摘要(英)

Due to illumination and temperature, intermittent characteristics of a PV power system cause negative impacts on power systems. This thesis presents an intelligent controller based on the recurrent probabilistic wavelet fuzzy neural network (RPWFNN) algorithm for the energy management system to mitigate the fluctuation of PV output power directly fed to the grid. The energy management system uses NextDrive′s data collection gateway Atto and integrates CHEM′s microgrid system. Atto collects microgrid power information every minute and uploads it to the cloud. In grid-connected mode, Atto performs PV power smoothing algorithm. It controls the charging or discharging of the battery energy storage system to smooth the power fluctuations of the grid. When the grid fails, the PV power system and battery energy storage system will provide a stable AC voltage, so that the microgrid can work normally in island mode. The network structure of the RPWFNN and its online learning algorithms are described in detail. In addition, battery life might be decreased by frequent charge-discharge cycling in PV power smoothing control. The method based on fuzzy logic is proposed in this thesis to reduce shallow cycles, and extends the battery life. Moreover, state of charge estimation using Coulomb counting method is adopted in the energy management of battery. According to the grid active power fluctuation limit set in this study, compared with other smooth control methods, RPWFNN not only meets the specifications, but also has the minimum battery energy capacity. Finally, simulated and experimental results of various PV variation scenarios verify the effectiveness of the proposed intelligent PV power smoothing control.

關鍵字(中)

★ 電能管理系統
★ 太陽光電系統
★ 電池儲能系統
★ 功率平滑化
★ 遞迴式機率小波模糊類神經網路
★ 電池狀態

關鍵字(英)

★ energy management system
★ PV power system
★ battery energy storage system
★ power smoothing
★ recurrent probabilistic wavelet fuzzy neural network
★ battery status

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VIII
表目錄 XIII
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
第二章微電網架構與控制策略 9
2.1 微電網架構 9
2.2 三相座標軸轉換 10
2.2.1 鎖相迴路 12
2.2.2 三相功率計算 13
2.3 變流器控制技術 13
2.3.1 電流控制 14
2.3.2 定功率控制 14
2.3.3 電壓頻率控制 15
2.4 太陽能電池最大功率點追蹤 16
2.5 微電網分級控制 18
第三章電能管理系統介紹 20
3.1 通訊架構 20
3.1.1 Modbus通訊協定 21
3.2 電能管理系統 25
3.2.1 追電模式 26
3.2.2 可備援模式 27
3.2.3 時間電價模式 28
3.2.4 通訊控制模式 28
3.2.5 太陽光電功率平滑化模式 28
3.3 硬體設備 29
3.3.1 NextDrive Atto 29
3.3.2 中興電工微電網系統 31
3.3.3 可程控直流電源供應器(具太陽能電池陣列模擬功能) 34
第四章遞迴式機率小波模糊類神經網路 38
4.1 遞迴式機率小波模糊類神經網路架構 38
4.2 遞迴式機率小波模糊類神經網路線上學習法則 42
4.3 遞迴式機率小波模糊類神經網路收斂性分析 45
第五章太陽光電功率平滑化控制技術探討 48
5.1 再生能源功率波動 48
5.2 電池電量計算 48
5.3 太陽光電功率平滑化方法 49
5.3.1 移動平均法 50
5.3.2 二階低通濾波法 51
5.3.3 遞迴式機率小波模糊類神經網路 51
5.3.4 太陽光電功率平滑化方法之比較 52
5.4 電池充放電探討 58
5.5 以模糊邏輯減少電池淺充淺放次數 59
5.5.1 減少電池淺充淺放次數控制法 60
5.5.2 模糊邏輯 61
5.5.3 以模糊邏輯減少電池淺充淺放次數之效果 64
第六章太陽光電功率平滑化模擬與實作成果 69
6.1 系統簡介 69
6.2 不同太陽光照度變化下功率平滑化之模擬 70
6.2.1 照度變化700W/m2-900W/m2-1000W/m2-900W/m2 71
6.2.2 照度變化900W/m2-1000W/m2-900W/m2-800W/m2 75
6.2.3 照度變化700W/m2-800W/m2-700W/m2-900W/m2-1000W/m2 79
6.2.4 不同照度變化之電池狀態分析 83
6.3 不同太陽光照度變化下功率平滑化之實作 85
6.3.1 照度變化700W/m2-900W/m2-1000W/m2-900W/m2 89
6.3.2 照度變化900W/m2-1000W/m2-900W/m2-800W/m2 91
6.3.3 照度變化700W/m2-800W/m2-700W/m2-900W/m2-1000W/m2 93
6.3.4 不同照度變化之電池狀態分析 95
第七章結論與未來研究方向 96
7.1 結論 96
7.2 未來研究方向 97
參考文獻 98
作者簡歷 104

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2020-8-13

推文