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研究生:許博鈞
研究生(外文):Po-Chun Hsu
論文名稱:以RSCMAC結合區域氣象資料為基礎之風力發電量預測
論文名稱(外文):A RSCMAC Based Forecasting for Wind Power from Local Weather Information
指導教授:江青瓚
學位類別:碩士
校院名稱:健行科技大學
系所名稱:電機工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:87
中文關鍵詞:風力發電監測系統回授最簡類化型小腦模型控制器區域電網最佳調度
外文關鍵詞:wind powermonitoring systemRSCMACregional gridoptimal scheduling
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近年來,由於化石燃料藏量有限與溫室效應等環境變遷問題,迫使世界各國莫不極力關注並嘗試尋求解決之道,台灣身為世界社區的一份子,自是不能置身事外,其中發展再生能源便是方法之一。近年來,政府不僅通過再生能源條例,更全力推動再生能源產業,提出”千架海陸風力機計畫”。因風力屬於間歇性能源,隨著再生能源滲透率的逐漸攀升,若缺乏前瞻性的規劃設計與適當的運轉策略,勢必會影響電力系統供電可靠度與供電品質,並降低再生能源的使用率。若風機之輸出功率能被預測,則其電力就有可能調度或有其他策略來穩定併聯電網,本研究選定澎湖中屯風機公園為測試場址,公園內共架設8支德國ENERCON E-40 600kW風力發電機。為收集公園內之風速情形在周遭架設3組氣象監測站,並以回授最簡類化型小腦模型控制器(RSCMAC)為基礎,使用台電提供之澎湖中屯風力發電機監測系統資料與區域性氣象監測系統,開發三種不同的極短期(1小時)風機發電量預測模型。三種模型訓練及測試結果呈現優異的成果,展現模型的可用性及穩定性。未來此3種高準確度風機輸出功率預測模型,可提供區域電網風力發電之最佳調度,以達風力發電之最大使用效益,亦可提高風力機之裝置容量。
In recent years, due to limited reserves of fossil fuels and greenhouse gases and other environmental changes, forcing the world have to focus on and try to find a solution. Taiwan as a part of the world community, certainly cannot stay out of the issue, one of the solutions is to develop renewable energy. In recent years, the government not only pass the renewable energy regulations, but also put more efforts to promote the renewable energy industry, proposed “Thousands of sea & land wind turbine project”. The wind power is intermittent energy, with the gradual increase in renewable energy penetration, if lack of forward-looking planning design and appropriate operational strategy, will definitely affect the power supply reliability and power quality of the power system, then reduce the use of renewable energy. If the output power of the wind turbine can be predicted, then its power is possible to be dispatched or have other strategy to stabilize the grid. The research selected Penghu Zhongtun wind turbine park as the test site, there are total 8 German ENERCON E-40 600kW wind turbines installed in the park. In order to collect wind speed information in the park, 3 meteorological monitoring stations were set up in the neighborhood. Based on RSCMAC, using Penghu Zhongtun wind turbine monitoring system data provided by Taipower and regional meteorological monitoring system, three different extremely-short(1 hour) wind turbine power generation prediction models were developed. These three model training and test results showed excellent performance, displayed the availability and stability of the models. In the future, these three high-accuracy wind turbine output power prediction models can provide the optimal dispatch of wind power in the regional grid to achieve the maximum utilized efficiency and also to increase the wind turbine installation capacity.
摘  要 i
Abstract ii
誌 謝 iv
目  錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文方法 2
1.4 論文架構 3
第二章 回歸型最簡類化型小腦模型控制器之介紹 5
2.1 回歸型最簡類化型小腦模型控制器(RSCMAC) 5
2.1.1 回歸型最簡類化型小腦模型控制器之基礎架構 6
2.1.2 回歸型最簡類化型小腦模型控制器之學習法則 12
2.2 回歸型最簡類化型小腦模型控制器(RSCMAC)程式訓練使用介紹 14
2.3 監測資料收集與分析 16
2.3.1 資料除錯與正規化處理 19
2.4 小結 21
第三章 以中屯風機資料為基礎之極短期風機輸出功率預測模型 22
3.1 中屯風機監測系統資料 22
3.2 以中屯風機資料為基礎之輸出功率模型 25
3.2.1 風機輸出功率預測模型訓練過程 26
3.2.2 風機輸出功率預測模型訓練結果 29
3.2.3 風機輸出功率預測模型測試結果 34
3.3 風機輸出功率模型訓練與測試之誤差統計 40
3.4 小結 43
第四章 以氣象站資料為基礎之極短期風機輸出功率預測模型 44
4.1 區域氣象站監測系統資料 44
4.2 以氣象站資料為基礎之輸出功率模型 47
4.2.1 區域氣象站輸出功率預測模型訓練過程 48
4.2.2 區域氣象站輸出功率預測模型訓練結果 50
4.2.3 區域氣象站輸出功率預測模型測試結果 55
4.3 氣象站輸出功率預測模型訓練與測試之誤差統計 60
4.4 小結 63
第五章 以中屯風機結合氣象站資料為基礎之極短期風機輸出功率預測模型 64
5.1 以中屯風機結合氣象站監測系統資料為基礎之輸出功率模型 64
5.2 結合風機與氣象站輸出功率預測模擬結果 67
5.2.1 結合風機與氣象站輸出功率預測模型訓練結果 67
5.2.2 結合風機與氣象站輸出功率預測模型測試結果 72
5.3 風機與氣象站輸出功率預測模型訓練與測試之誤差統計 77
5.4 相關文獻比較 80
5.5 小結 81
第六章 結論 82
6.1 研究問題討論與解決方法 82
6.2 研究心得 83
6.3 未來展望 84
參考文獻 85
簡 歷 87
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