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系統識別號 U0026-3006201116162900
論文名稱(中文) 基於經驗模態分解的局部放電信號小波分析
論文名稱(英文) Partial Discharge Signal Analysis Using the Empirical Mode Decomposition with Wavelet Transform
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 99
學期 2
出版年 100
研究生(中文) 林美妍
研究生(英文) Mei-Yan Lin
學號 N26980165
學位類別 碩士
語文別 中文
論文頁數 77頁
口試委員 指導教授-戴政祺
口試委員-黃世杰
口試委員-林志隆
口試委員-楊慶隆
口試委員-張簡嘉壬
中文關鍵字 局部放電  經驗模態分解法  小波轉換法  基於經驗模態分解的小波分析法  訊雜比 
英文關鍵字 Partial discharge (PD)  empirical mode decomposition (EMD)  wavelet transform (WT)  empirical mode decomposition with wavelet transform (EMD-WT)  intrinsic mode functions (IMFs)  signal to noise ratio (SNR) 
學科別分類
中文摘要 局部放電訊號的檢測是診斷電力設備狀況的一項重要技術,近年來許多研究結果顯示小波分析法對於局部放電訊號的處理在雜訊抑制上有很好的表現。但是,當雜訊干擾變大而導致訊雜比下降時,小波分析法的結果就不甚理想。經驗模態分解法透過篩選的過程將本質模態函數擷取出來,這些本質模態函數是由原始訊號擷取出來,所以對於非線性和非穩態訊號有較好的適應性。因此,結合經驗模態法的自適性和小波轉換法的去雜訊能力來分析局部放電訊號,將放電特徵訊號從複雜訊號中擷取出來。相較於只有單一使用小波分析法,基於經驗模態分解的小波轉換法在雜訊抑制上有較好的表現。經驗模態分解法可以將特徵訊號擷取出來並利用小波分析將雜訊去除。分析的結果顯示,基於經驗模態分解的小波分析法在雜訊干擾嚴重時,仍然可以將訊號分析出來,具有較好的抗雜訊能力。
英文摘要 The partial discharge (PD) detection is one of the most important techniques to evaluate the condition of power equipment. Recent researches show that the wavelet transform (WT) has good performance in noise reduction for PD signal analysis. However, if the signal-to-noise ratio (SNR) decreases, the performance of noise reduction used the WT method will become worse. The Empirical mode decomposition (EMD) is a signal processing method to extract intrinsic mode functions (IMFs) form the signal, and it has good adaptivity for non-stationary and nonlinear signal analysis. This paper uses the advantage of EMD and combines with the wavelet transform (EMD-WT) to extract partial discharge (PD) signals in noises. Compare to the WT method, the EMD-WT has better performance for noise reduction. It has been verified that the EMD-WT method can preserve more information even though the SNR is low. The results show that the EMD-WT is suitable for PD denoising in a noisy environment.
論文目次 摘 要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1研究背景 1
1.2相關文獻回顧 2
1.3研究動機與目的 3
1.4論文章節概要 4
第二章 局部放電檢測方法和音射原理 5
2.1局部放電原理 5
2.1.1局部放電成因 5
2.1.2局部放電種類 6
2.2局部放電檢測方式 7
2.2.1電氣檢測法 7
2.2.2非電氣檢測法 7
2.3音射訊號原理 9
2.3.1音射訊號參數 9
2.3.2突發型和連續型音射訊號 10
第三章 小波轉換與經驗模態分解法原理 11
3.1 小波原理 11
3.1.1連續小波 11
3.1.2離散化的連續小波 12
3.1.3多分辨率分析 13
3.1.4訊號的分解和合成 16
3.2 小波轉換與雜訊濾除 18
3.2.1選擇合適的小波函數 18
3.2.2訊號的分解階層 19
3.2.3閾值化與閾值 20
3.3經驗模態分解理論 22
3.3.1本質模態函數 23
3.3.2經驗模態分解 23
3.4基於經驗模態分解的小波分析 25
3.5小波能量分析 26
3.5.1 Morlet小波轉換 27
3.5.2小波尺度選擇與功率分析 28
第四章 訊號分析結果與討論 30
4.1測試的局部放電訊號 30
4.1.1產生測試的局部放電訊號 30
4.1.2測試的局部放電訊號分析 31
4.2實際局部放電訊號 39
4.2.1 實際的局部放電訊號 39
4.2.1 加入雜訊的實際局部放電訊號 56
第五章 結論與未來發展 71
5.1結論 71
5.2未來展望 71
參考文獻 73
自述 77
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