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研究生:楊其昌
研究生(外文):Chi-Chang Yang
論文名稱:高斯混合模型在風機預兆式健康管理上的應用研究
論文名稱(外文):Study on the Application of Gaussian Mixture Model in the Prognostic and Health Management of Wind Turbine
指導教授:蔡進發蔡進發引用關係
指導教授(外文):Jing-Fa Tsai
口試委員:王昭男邵揮洲陳一成
口試委員(外文):Chao-Nan WangHeiu-Jou Shaw
口試日期:2016-07-22
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工程科學及海洋工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:95
中文關鍵詞:風力發電機高斯混合模型預兆式健康管理
外文關鍵詞:wind turbineGaussian mixture modelPrognostic and health management
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本研究以高斯混合模型(Gaussian Mixture Model)為核心建立一套風機預兆式健康診斷與預測的方法,此方法包含:採用DBSCAN(Density-Based Spatial Clustering of Applications with Noise)對風機原始參數進行過濾,高斯混合模型建立風機營運性能模型,以信心值來表示風機的健康狀態,再利用迴歸分析來預測風機未來營運的健康信心值。
本研究利用所建立的預兆式診斷方法對台電林口四號風機的資料進行分析,分析的結果顯示,此風機在一般正常運作的情況下,健康狀況信心值約在0.4至0.8之間,但風機營運出現了異常狀況時,信心值大多低於0.4。但若就長時間的信心值變化而言,此風機在2013年至2015年這三年間是呈現穩定的狀態,即代表此風機在這三年間並無太大的性能衰退。另外,透過迴歸分析計算,可預測出此風機於2033年8月8日後,整體的健康狀況信心值將會下降到2013年平均值的兩個標準差以下,代表風機的性能可能在該時間衰退至不健康的狀態。


A prognostic and health management method based on the Gaussian mixture model is proposed in this study to analyze and predict the performance of wind turbine. The proposed method includes preprocessing the raw data of wind turbines by DBCSAN (Density-Based Spatial Clustering of Applications with Noise), building the model on operating performance of wind turbines by GMM (Gaussian Mixture Model), indicating the operating performance by the CV (Confidence Value), and predicting the CV in the future by regression analysis.
The proposed method was applied to analyze the performance data of the Wind Turbine No.4 of Taiwan Power Company at Linkou District. The analysis showed that the CV is between 0.4 and 0.8 in the normal condition and is smaller than 0.4 in the abnormal condition. The CV of the wind turbine is stable between 2013 and 2015. That is, the performance of this wind turbine was not degrading obviously. Furthermore, by regression analysis, the trend of CV will reduce to 0.66 which is out of 2 standard deviations below the mean of 2013 on August 8, 2033. It means that the wind turbine may be probably unhealthy at that moment.


摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VIII
第一章、緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 2
1.3 研究內容 3
1.4 論文架構 3
第二章、高斯混合模型 4
2.1 高斯混合模型 4
2.2 期望值最大化演算法 5
2.3 信心值計算 6
2.4 DBSCAN 9
2.5 迴歸分析 10
第三章、資料的前處理 13
3.1 資料介紹 13
3.1.1 資料來源 13
3.1.2 資料分析 13
3.2 資料過濾 14
3.2.1 依參數過濾 14
3.2.1 DBSCAN 15
第四章、風機健康狀況分析及預測 17
4.1 以經過過濾的資料作為訓練資料進行健康狀況分析 17
4.1.1 以高斯混合模型近似訓練資料 17
4.1.2 以高斯混合模型近似單日資料且計算信心值 18
4.1.3 計算年度信心值曲線 20
4.1.4 信心值曲線平滑化 21
4.2 以自適應風速區間 (adaptive wind speed interval) 進行健康狀況分析 21
4.2.1 自適應風速範圍選取訓練資料 21
4.2.2 計算健康狀況信心值 22
4.3 迴歸分析及信心值曲線預測 24
4.3.1 利用指數函數計算迴歸線 24
4.3.2 利用指數函數迴歸計算信心值曲線衰退速度 25
第五章、結論與建議 28
5.1 結論 28
5.2 建議 29
參考文獻 30
附圖 32
附表 59
原始程式碼 61



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