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研究生:張榕帟
研究生(外文):CHANG,ZONG-YI
論文名稱:使用卡爾曼濾波器於風速估測與狀態評估
論文名稱(外文):Estimation on Wind Speed and Status AssessmentUsing Kalman Filtering
指導教授:萬欽德
指導教授(外文):WANN,CHIN-DER
口試委員:李建德萬欽德洪金車楊新雄
口試委員(外文):LEE,JIANN-DERWANN,CHIN-DERHUNG,KING-CHUYANG,HSIN-HSYONG
口試日期:2017-10-03
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:電腦與通訊工程系碩士班
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:106
語文別:中文
論文頁數:44
中文關鍵詞:風速儀風速估測風加速度估測卡爾曼濾波器狀態評估
外文關鍵詞:AnemometerWind Speed EstimationWind Acceleration EstimationKalman FilterStatus Assessment
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風速量測被應用在許多環境,風力過大可能產生災害,因此偵測風速對於安
全是很重要的因素之一。風速儀監控風力是最常被使用的工具,當前警示系統採
用一定時間內之平均風速或瞬間最大風速,對於量測數據可能有被平均化或因偵
測到最大風速而啟動警示系統,造成沒有即時提供正確決策產生災害。為了達
到更精準提早發布警示或降低誤判啟動警示,本文中針對卡爾曼濾波器(Kalman
Filter),使用具遞歸之估測及修正法,來估測風速及風加速度。由於使用平均風
速可能會有可信度不足的情況,過高或過低之量測值經過平均化處理後,都可能
導致警示系統決策反應跟不上當前狀況。經過卡爾曼濾波器處理過後,將利用估
測的風速及風加速度,做為決策的參數之一,提供在天候不佳時決策系統之判斷
依據,除了以加速度判斷風速劇烈變化外,對於風速估測值也有較好的追蹤效
果,由不同類型的風速呈現出以平均風速及瞬間最大風速做為決策的問題點。對
於卡爾曼濾波器處理後之風參數,決策上因應三種不同類型的風參數,分別為風
速漸強到達警示條件的漸強風速,到達臨界時忽高忽低風速的臨界風速以及風速
劇烈變動的劇烈變動風速,由三種不同類型風參數經卡爾曼濾波器處理後,並結
合滑動視窗的訊號處理,給予合適的狀態評估,使得決策系統更能夠提早因應和
警示,降低誤判的機會,避免強風對列車行駛的安全危害。有了更精準的判斷,
除了增加風速預警機制精準性外,也可經由估測後之風加速度了解是否有風速劇
烈變化等特殊現象,並提早關閉閘道或停止車輛行駛達到將災害減至最低的功
用。
Wind speed measuring are used in many environments where winds are too large
to cause disasters, so detecting wind speed is one of the most important factors for safety. Current warning system works out with an average or instant maximum wind speed as a parameter. The measurement data might be desalinated or activated by detecting maximum wind speed and lead to tragedy without making a current decision. To improve the accuracy or lower the probability of misjudgment, we use Kalman Filter to estimate wind speed and wind acceleration on its recursive estimation and correction formula. Average wind speed estimation might have credibility problem by averaging critical values. It could cause the detention and inappropriate to the warning system and make a unsuitable decision of current situation. After Kalman Filtering, wind speed and wind acceleration were used as decision parameter. We observed the un tness using average or instant maximum wind speed as a decision parameter from different experiment. The wind parameter after Kalman Filtering can be suitable to different kinds of situations. The results show three
different kinds of wind parameter processed by Kalman Filter and combined with
sliding window signal processing giving suitable feedback to reduce the probability of misjudgment, avoiding the dangerous to drive from strong wind. By using the proposed decision assisted system, we can improve the accuracy of warning system and improve the safety factors of related applications.
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
一、緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
二、風速擷取與資料處理. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 風速與風加速度. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 風速觀測標準. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 風速儀設備及應用環境. . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4 實驗環境之風速擷取. . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4.1 LabVIEW軟體平台. . . . . . . . . . . . . . . . . . . . . . . . 10
2.4.2 MyDAQ擷取裝置. . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5 基本動態系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
三、使用卡爾曼濾波器估測風參數. . . . . . . . . . . . . . . . . . . . . . . . 18
3.1 一般之風速警示系統. . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1.1 使用平均風速當決策點. . . . . . . . . . . . . . . . . . . . . . 18
3.1.2 使用瞬間最大風速當決策點. . . . . . . . . . . . . . . . . . . 19
3.2 卡爾曼濾波器運算架構. . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 風速及警示條件關聯. . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4 新風速警示系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
四、實驗與結果討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1 系統架構及風速類型. . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2 場景一:漸強風速實驗結果. . . . . . . . . . . . . . . . . . . . . . . . 34
4.3 場景二:臨界風速實驗結果. . . . . . . . . . . . . . . . . . . . . . . . 35
4.4 場景三:劇烈陣風實驗結果. . . . . . . . . . . . . . . . . . . . . . . . 36
4.5 實驗結果與探討. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
五、結論與未來方向. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.2 未來方向與建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
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