基於變高斯濾波模糊神經網路之太陽能發電系統輸出平滑控制策略

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姓名

董學易(TUNG HSUEH-YI) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於變高斯濾波模糊神經網路之太陽能發電系統輸出平滑控制策略
(Output Smoothing Control Strategy for Solar Power Generation Systems Based on Improved Gaussian Filter and Fuzzy Neural Network)

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摘要(中)

近年來全球對於發展和應用再生能源越來越關注，尤其是太陽能(Photovoltaics, PV)技術。然而太陽能發電具有高度的間歇性，可能會影響電力系統的電力品質，因此結合電池儲能系統(Battery Energy Storage System, BESS）來提高電網的穩定性成為解決方案之一。但受限於併網法規和儲能系統成本高昂，使用平滑濾波器與BESS相結合來降低成本和穩定輸出功率已成為一種受到廣泛關注的方法。
本文提出的變高斯濾波算法中除了能有效減少時間延遲現象，貼合原功率輸出波型，也擁有較好的平滑程度。此外本文提出一種模糊類神經網路(Fuzzy Neural Network)，透過輸入電池電量、負載使用量以及太陽能功率並模糊化後，獲得各電網的分配比例。本文場域利用儲能系統、太陽能系統與負載整合成三區多微電網系統，結合太陽能平滑並控制統合各區輸出功率，使得系統總體輸出回市電端能有效平滑且符合台灣電力公司規定，透過獲得的分配比例能有效平衡三區微電網的電池電量並使其保持一致。此控制策略除了能有效提高市電端的電力品質，且能維持電量的平衡讓整體電網的使用時間提升。

本文模擬採用MATLAB/Simulink模擬軟體來模擬與比較本文平滑化控制策略的可行性，並使用C語言撰寫相關類神經演算法與平滑算法，最終在實驗場域驗證其控制策略之結果。

摘要(英)

In recent years, there has been increasing global interest in the development and application of renewable energy, particularly Photovoltaics (PV) technology. However, due to the intermittent nature of solar power generation, it may cause problems such as power system faults or blackouts. Combining Battery Energy Storage System (BESS) to improve the stability of the grid has become one of the solutions. However, due to regulatory constraints and the high cost of energy storage systems, combining smoothing filters with BESS to reduce costs and stabilize output power has become a widely recognized method.
This article proposes a variable Gaussian filter algorithm that not only effectively reduces time delays and fits the original power output waveform but also has good smoothness. In addition, a Fuzzy Neural Network is proposed in this article, which inputs the battery power, load usage, and solar power and obtains the allocation ratio of each power grid after fuzzification. The experimental field of this article uses the storage system, PV system, and load integration into three micro-grid systems, combined with solar smoothing and control, to integrate the output power of the three micro-grid systems, enabling effective smoothing and meeting the ramp rate set by Taiwan Power Company. Through the obtained allocation ratio, the battery power of the three micro-grids can be effectively balanced and kept consistent. This control strategy not only effectively improves the power quality at the grid end but also maintains the balance of power, improving the overall use time of the power grid.
This article uses Matlab/Simulink simulation software to simulate and compare the feasibility of the smoothing control strategy proposed in this article. The relevant neural network algorithms and smoothing algorithms are written in C language, and the results of the control strategy are verified in the experimental field.

關鍵字(中)

★ 多微電網
★ 太陽能平滑化
★ 電力調度策略

關鍵字(英)

★ multi-microgrids
★ solar power smoothing method
★ power dispatch strategy

論文目次

目錄
論文摘要 I
ABSTRACT III
致謝 V
目錄 VI
圖目錄 X
表目錄 XIV
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 5
第二章太陽能平滑化方法與現行規範 6
2-1 移動平均濾波器 6
2-2 雙移動平均濾波器 7
2-3 指數移動平均濾波器 8
2-4 一維高斯濾波器 9
2-5 變高斯濾波器 11
2-6 比較指標與法規 13
2-6-1 功率變動率 13
2-6-2 峰谷平均曲率指標 15
2-6-3 平滑度指數 15
2-6-4 均方根誤差 16
2-6-5 平均絕對誤差 17
第三章平滑後輸出功率調節系統與控制法 18
3-1 系統介紹 18
3-2 控制調度架構 19
3-3 模糊類神經網路控制架構與方法 22
3-3-1 模糊控制理論 23
3-3-2 類神經網路理論 30
3-3-3 模糊類神經網路架構 34
3-3-4 模糊類神經網路學習法則 38
第四章系統模擬結果與比較 41
4-1 方法結果圖形分析 42
4-1-1 高斯濾波器補償法比較 42
4-1-2 變高斯濾波器比較 45
4-1-3 移動平均法比較 48
4-1-4 雙移動平均法比較 50
4-1-5 指數移動平均法比較 52
4-2 指標分析比較 55
4-2-1 功率變動率比較 55
4-2-2 峰谷平均曲率指標比較 58
4-2-3 平滑度指數比較 61
4-2-4 均方根誤差比較 63
4-2-5 平均絕對誤差比較 66
4-3 控制策略模擬比較 68
4-3-1 CASE1個別平滑 70
4-3-2 CASE2模糊類神經控制策略 74
第五章系統實作驗證結果 76
5-1 實驗場域介紹 76
5-2 控制通訊介紹 82
5-2-1 MODBUS RTU 83
5-2-2 MODBUS TCP 84
5-2-3 CONTROLLER AREA NETWORK 86
5-3 實驗場域結果分析 87
第六章結論與未來研究方向 90
6-1 結論 90
6-2 未來研究方向 91
參考文獻 92

參考文獻

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指導教授

陳正一

審核日期

2023-7-17

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