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研究生:張珈豪
研究生(外文):Chia-Hao Chang
論文名稱:利用類神經網路與小波轉換辨識多重電力品質事件
論文名稱(外文):Recognition of the Multiple Power Quality Events by Neural Networks and Wavelet Transform
指導教授:王朝興王朝興引用關係
指導教授(外文):Chau-Shing Wang
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:電機工程學系
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:2008
畢業學年度:96
語文別:中文
論文頁數:88
中文關鍵詞:小波轉換電力品質LabVIEW軟體類神經網路
外文關鍵詞:wavelet transformpower quality eventLabVIEWneural network
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本研究針對多重電力品質事件之偵測與分析進行研究。隨著科技的進步發展,精密的生產設備與電子儀器被大量的使用,使得供電品質的要求日益升高,然而某些電器設備可能產生干擾,而影響其他設備。電力系統中不同之電力品質事件之干擾,皆有許多不同的解決措施。為了提升電力品質並找尋造成電力品質干擾之原因,需廣泛且長時間的監測資料,並對資料加以分析辨識,作為改善電力品質的參考。
在實際系統上,經常發生一電力品質事故同時具有多重電力品質干擾因素。故本研究將針對常見之電力品質事件,發展一套可同時辨識多重電力品質事件之偵測系統。為補充量測數據之不足,本文發展以LabVIEW軟體為基礎之具圖形介面的電力品質模擬系統,可模擬產生電力品質事件含有多種電力品質干擾波形,將之以圖形及相關數據表現,以提供方便使用之人機介面。將做為發展電力品質辨識與分析系統之測試資料庫,亦可提供作為教學及實驗之工具。由此模擬系統產生之干擾事件經由類神經網路,針對這些電力品質事件做出快速且準確的辨識。
This paper aims to develop a system to perform diagnostics and analyze on the multiple power quality events. With rapid developments of technology and wide uses of precise equipment as well as delicate electronic devices, much higher power quality is required nowadays. The newly developed and widely used electric devices, while themselves are often the sources responsible for producing variant disturbance, are becoming more and more sensitive to power quality variations. Considering cost and performance, different power quality disturbance in the power system requires solving method with differential approaches. To improve the power quality and find out the causes of the power transients, it needs to monitor the power signals extensively for a long time, and the presence of multiple power quality events is natural in many power systems. Therefore, this paper develop a power quality disturbances classification system, which is capable of classifying multiple power quality disturbances. We use Neural Networks, and Fuzzy Neural Networks to recognize the power quality event. We also develop a power quality event simulation by LabVIEW, which can simulate the multiple events. It’s very suitable to be used as an auxiliary tool when the students study the electric power quality course.
摘 要 i
Abstract ii
謝 誌 iii
目 錄 iv
圖目錄 viii
表目錄 xi
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 研究方法 3
1.4 文獻探討 3
1.5論文組織架構 5
第二章 電力品質之監測辨識與發展 7
2.1 簡介 7
2.2 電力品質事件與其影響性 8
2.2.1 電壓中斷 8
2.2.2 電壓驟升 10
2.2.3 電壓驟降 11
2.2.4 諧波失真 11
2.2.5 電壓閃爍 12
2.2.6 震盪暫態 13
2.3 結論 14
第三章 小波轉換方法與電力信號分析 15
3.1 簡介 15
3.2 既有之信號處理方法 16
3.2.1 傅立葉分析 16
3.2.2 短時傅立葉轉換 17
3.2.3 小波轉換(Wavelet Transform, WT) 18
3.3 離散小波轉換(Discrete Wavelet transform, DWT) 21
3.3.1多層解析分析(Multi-Resolution Analysis, MRA )21
3.3.2 離散小波轉換與多層解析 23
第四章 類神經網路理論 27
4.1類神經網路簡介 28
4.1.1 人工神經元模型 29
4.1.2 類神經網路架構 30
4.2 倒傳遞網路 34
4.2.1 網路架構 34
4.3 機率類神經網路 36
4.3.1 網路架構 36
4.4 徑向基網路 37
4.4.1 網路架構 37
4.5 模糊類神經網路 38
4.5.1 模糊理論 39
4.5.2 模糊類神經網路架構 41
4.5.3 類神經網路與模糊系統之共通點及差異性 43
4.5.4 常見模糊類神經網路之應用 46
第五章 LabVIEW信號產生系統 47
5.1 簡介 47
5.2 軟體架構 48
5.3 諧波失真波形模擬 49
5.4 電壓閃爍波形模擬 51
5.5 電壓驟降、驟升、中斷波形模擬 52
5.6 突波波形模擬 55
5.7 綜合事件波形模擬 56
5.8 類比數位資料轉換器簡介 61
5.8.1 類比數位資料轉換器簡介 62
5.9 結論 65
第六章 實驗結果與分析 67
6.1 小波轉換結果 67
6.2 類神經網路辨識分析 71
6.2.1 倒傳遞網路(BPN)辨識結果 72
6.2.2 機率神經網路(PNN)辨識結果 75
6.2.3徑向基網路(RBN)辨識結果 76
6.2.4 模糊倒傳遞網路(FBPN)辨識結果 77
6.2.5 各種網路辨識結果比較 79
第七章 結論與展望 81
7.1 結論 81
7.2 未來展望 82
參考文獻 83

圖2.1 電壓中斷波形..........................10
圖2.2 電壓驟昇波形..........................10
圖2.3 電壓驟降波形...........................11
圖2.4 諧波失真波形...........................12
圖2.5 電壓閃爍波形...........................13
圖2.6 震盪暫態波形...........................14
圖3.1 短時傅立葉轉換時頻平面圖...................18
圖3.2 小波轉換時頻平面圖........................19
圖3.3 小波之尺度變化...........................20
圖3.4 小波於訊號上之平移.......................20
圖3.5 訊號的濾波與合成........................24
圖4.1 生物神經元結構.........................29
圖4.2 人工神經元處理模式......................30
圖4.3 具Xi 個輸入的神經元.....................31
圖4.4 硬極限函數..............................31
圖4.5 線性函數...............................31
圖4.6 對數S 形函數.............................32
圖4.7 雙曲正切S 形函數........................32
圖4.8 多層前饋網路架構.........................35
圖4.9 機率類神經網路架構.......................37
圖4.10 徑向基網路架構..........................38
圖4.11 三角形歸屬函數..........................40
圖4.12 輸入特徵之歸屬函數......................41
圖4.13 模糊類神經網路架構......................42
圖5.1 電力品質波形產生架構圖...................48
圖5.2 軟體流程圖............................49
圖5.3 諧波失真波形版面.........................50
圖5.4 諧波失真波形之LabVIEW 程式...............51
圖5.5 電壓閃爍波形版面.........................52
圖5.6 電壓閃爍波形之LabVIEW 程式................52
圖5.7 電壓驟降波形版面.........................53
圖5.8 電壓驟降波形程式.........................54
圖5.9 電壓驟升波形模擬.........................54
圖5.10 電壓中斷波形模擬........................54
圖5.11 電壓突波波形版面........................55
圖5.12 電壓突波波形程式........................56
圖5.13 驟降包含諧波之波形...........................................57
圖5.14 驟升包含諧波之波形......................57
圖5.15 中斷包含諧波之波形......................58
圖5.16 閃爍包含諧波之波形......................58
圖5.17 諧波包含突波之波形......................58
圖5.18 正常波形產生版面........................59
圖5.19 正常波形產生程式........................60
圖5.20 即時波形插入版面........................60
圖5.21 隨機波形插入程式........................61
圖5.22 Excel 圖檔............................61
圖5.23 類比/數位資料轉換.......................63
圖5.24 解析度與訊號精確性......................65
圖6.1 電力品質事件波形小波轉換能量圖............70
圖6.2 類神經網路之辨識流程....................73
圖6.3 倒傳遞網路訓練收斂圖.....................74
圖6.4 模糊倒傳遞網路訓練收斂圖.................78
表 2.1 不良電力品質問題分類...................9
表3.1 小波轉換與傅立葉轉換的比較...............26
表6.1 D8 小波各層響應頻率表...................68
表6.2 倒傳遞網路之辨識結果....................75
表6.3 機率神經網路之辨識結果..................76
表6.4 徑向基網路之辨識結果....................77
表6.5 模糊倒傳遞網路之辨識結果................79
表6.6 倒傳遞與模糊倒傳遞辨識度比較............80
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