臺灣博碩士論文加值系統

　　由於Hilbert-Huang轉換具有優異之時頻定位能力，本文之研究主旨即在於將此轉換應用於電力訊號之時頻分析，其中包含經驗模態分解法與Hilbert頻譜分析等兩個主要步驟之構建。而於經驗模態分解法中，其負責將訊號分解成數個本質模態函數之組合，至於Hilbert頻譜分析則於Hilbert轉換後，求得每一本質模態函數之瞬時頻率與振幅，再藉由Hilbert頻譜圖之展現，協助掌握電力品質訊號之時頻分佈。又為驗證此時頻分析方法所具有之優異解析度，本文並將本方法應用於電壓中斷、電壓驟降、電壓突昇、電容切換暫態、電流諧波及電壓閃爍等電力訊號之分析，並與S轉換的分析結果作比較，測試結果佐證該方法確具良好之時頻定位效能，極適用於電力訊號之分析與檢測。

　　Thanks to the time-frequency localization capability of Hilbert-Huang Transform (HHT), the theme of this thesis is thus focused on the application of such transform for the analysis of power quality signals. The HHT algorithm consists of two major computation procedures that include Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). While EMD is responsible for the decomposition of signals into a collection of intrinsic mode functions (IMF), HSA describes the time-frequency distribution with the Hilbert spectrum representation with the aid of Hilbert transform. In order to confirm the feasibility of the method, this approach has been utilized to analyze several power disturbance signals, including the voltage interruptions, voltage sags, voltage swells, capacitor switching-transients, current harmonics, and voltage flickers. Test results consolidate the proposed method for the application considered.

中文摘要　I
英文摘要　II
致謝　III
目錄　IV
表目錄　VI
圖目錄　VII
第一章 緒論　1
1.1 研究背景　1
1.2 研究方法　3
1.3 論文架構　4
第二章 時頻分析相關演算法　5
2.1 短時Fourier轉換　5
2.2 小波轉換　8
2.3 S轉換　13
2.4 Wigner-Ville分佈　19
第三章 Hilbert-Huang轉換演算法　22
3.1 Hilbert轉換　22
3.2 Hilbert-Huang轉換　27
3.2.1 經驗模態分解　27
3.2.1.1 本質模態函數　28
3.2.1.2 篩選程序　30
3.2.1.3 經驗模態分解法之完整性　37
3.2.1.4 經驗模態分解法之正交性　39
3.2.1.5 間歇性檢查　40
3.2.2 Hilbert頻譜分析　43
3.3 分析與討論　48
第四章 模擬與分析　50
4.1 電壓中斷　52
4.2 電壓驟降　56
4.3 電壓突昇　60
4.4 電流諧波　64
4.5 電容切換暫態　68
4.6 電壓閃爍　72
第五章 結論與未來研究方向　76
5.1 結論　76
5.2 未來研究方向　77
參考文獻　78
附錄A　82
作者簡介　84

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