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研究生:翁睦盛
研究生(外文):Mu-Shen Weng
論文名稱:以小波轉換消除電力品質暫態信號雜訊方法之研究與比較
論文名稱(外文):A Study and Comparison on De-noising of Power Quality Transient Signal with Wavelet Transform
指導教授:楊宏澤楊宏澤引用關係
指導教授(外文):Hong-Tzer Yang
學位類別:碩士
校院名稱:中原大學
系所名稱:電機工程研究所
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:2002
畢業學年度:90
語文別:中文
論文頁數:109
中文關鍵詞:雜訊電力品質小波轉換
外文關鍵詞:noisepower qualitywavelet transform
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隨著高科技產業的快速發展,精密的生產設備與測試儀器大量的使用,供電品質要求乃日益昇高。然而,提高供電品質的首要工作在於廣泛收集有關不良電力品質的電力信號,因此,乃需要藉助電力品質監測器廣泛與長期的監測電力信號,以收集不良電力品質之電力信號,以瞭解不良電力品質的問題與發生原因,作為改善電力品質的參考依據。
在監測電力信號的過程中,所記錄之不良電力信號經由類比/數位轉換器、數位故障記錄器,再經波形資料傳送與量化等過程後,常會有誤差雜訊發生。這些雜訊通常是造成電力品質暫態監測器發生誤警訊的原因之一。為有效提升電力品質暫態監測器之準確率,勢必要有一有效之消除電力品質暫態信號中雜訊的方法,以提高電力品質暫態監測的能力。
對於電力品質暫態信號的處理,由於傳統傅立葉轉換應用於高頻暫態信號分析時,無法提供干擾發生的正確時間點,因此傳統傅立葉轉換於偵測暫態發生之時間點的能力明顯不足。因小波轉換在時域與頻域具有多重解析的能力和可變時-頻特性,當應用於高頻暫態時,經小波轉換後的信號有較高的時域解析度,可精確的在電力品質暫態信號中偵測出干擾發生的時間點,因此小波轉換常大量應用於電力品質暫態信號之偵測。然而由於上述雜訊的存在,使小波轉換在偵測電力品質暫態信號偵測上的準確性大為降低。就另一方面來說,為減少雜訊對信號偵測的影響,以小波轉換為基礎的方法亦被廣泛使用。
一般應用於小波轉換去除埋藏於電力信號中之雜訊時,經常以設定臨界值去除雜訊,但臨界值設定問題通常有賴經驗並與臨場狀況有關,因而顯得耗時費力,為解決臨界值設定的問題,本文提供三種去除雜訊演算法(適應性除雜法、統計假設檢定法與空間相關性法),可依據背景雜訊值的大小,自動調整其設定值。經由所提供之去除雜訊演算法後,可恢復小波轉換因雜訊影響偵測電力品質暫態信號的能力,進而提升電力品質監測器偵測暫態現象發生時間點的準確率。
為評估比較本文提供之三種以小波轉換消除電力品質暫態信號雜訊方法的可行性,本文利用MATLAB與電磁暫態程式套裝軟體模擬各種電力品質暫態信號作為測試信號,以確認所提供方法之準確性及效率。測試效果顯示,本文所提供之三種去雜訊演算法可如預期去除雜訊並正確偵測暫態現象發生的時間點。同時,在實現硬體架構時,本文建議使用運算式較為簡單之第三種空間相關性去雜訊演算法。
With the rapid developments of the high-tech industries as well as much more usages of the precise production equipments and test instruments, the far higher power quality (PQ) is demanded nowadays. However, the primary work of improving the power quality has to widely collect the power signals through the PQ monitoring instruments. Based the analysis of the PQ data collected, the causes and the problems of the PQ events can be inferred for the references of the PQ improvement.
In the process of monitoring power signals, the PQ related signals are recorded via the A/D converter, digital fault recorder, and waveform data transmission and quantification. Noises always exist in the process and contaminate the PQ signals collected. The noise-contaminated signals often result in false alarms of the PQ monitor, especially for the transient events. To enhance the accuracy of PQ transient monitor for the transient event detection, there must be a high-efficiency de-noising scheme for eliminating the influences of the noises riding on the signals. The accuracy of the PQ transient monitor can, therefore, be enhanced.
In processing the PQ transient signals, the traditional Fourier Transform (FT) extensively used for the observation of high-frequency transient signal, however, cannot determine precisely the time occurring points of the disturbance events. As a result, the FT is actually insufficient to detect the time occurring points of the transient events from the database for the PQ transient signals. In contrast, with the capabilities of multi-resolution and the characteristics of varying time-frequency windows on both the time and the frequency domains, the Wavelet Transform (WT) can indicate precisely the occurring points of the events, when the WT is applied to the high-frequency analysis using higher resolution in time domain. The WT is, therefore, extensively employed for the detection of transient signals in power systems. However, due to the existence of the noises as mentioned above, the accuracy of the WT in detection of transient signals is usually reduced greatly. On the other side, to reduce the influences of the noises riding on the signals, the WT-based de-noising approaches are also widely used.
While eliminating the noises on the power signals, a threshold is given to prune the noises in the WT-based de-noising approaches. Nevertheless, the setting of a threshold heavily relies on experiences and field circumstances. As a consequence, the de-noising work appears to be both time- and effort-consuming. To solve the problems of threshold value determination, three de-noising algorithms, including adaptive de-noising, hypothetical testing de-noising, and space correlating de-noising methods, are proposed in this thesis for automatically determining the thresholds in accordance with the background noises. Through the de-noising methods provided in the thesis for the PQ transient signal monitoring, the abilities of the WT in detecting and localizing the disturbances can hence be restored.
To evaluate and compare the feasibilities of the three WT based de-noising approaches for the PQ transient signals, the simulated data obtained from the MATLAB and Electro-Magnetic Transient Program (EMTP) programs as well as the filed data are used to test the three approaches. The testing results shows that the three de-noising approaches can overcome the influences of the noises successfully as expected. The occurring time points of the transient events can, therefore, accurately be detected and localized by the WT based approaches. The comparisons also reveal that if the hardware implementations are needed for the on-line de-noising applications, the third de-noising algorithm based on the space-correlating technologies is recommended for its simpler computing steps.
中文摘要
英文摘要
誌謝
目錄
圖目錄
表目錄
第一章 簡介
1-1 研究背景
1-2 研究動機
1-3 文獻回顧
1-4 研究方法
1-5 本論文貢獻
1-6 論文組織架構
第二章 電力品質問題
2-1 簡介
2-2 電壓暫態
2-3 電力品質問題種類
2-3-1 電力中斷
2-3-2 電壓驟降
2-3-3 電壓驟昇
2-3-4 電壓脈衝
2-3-5 諧波失真
2-4 本章結論
第三章 小波轉換
3-1 簡介
3-2 傳統信號分析方法
3-2-1 傅立葉分析
3-2-2 短時傅立葉轉換
3-3 小波轉換
3-3-1 連續小波轉換
3-3-2 離散小波轉換
3-4 小波轉換多重解析度分析
3-4-1 尺度函數
3-4-2 多重解析度分析
3-5 小波轉換與傅立葉轉換之比較
3-6 本章結論
第四章 小波轉換雜訊處理方法
4-1 簡介
4-2 方法一:適應性除雜法
4-2-1 目標
4-2-2 相異之臨界值函式
4-2-3 史坦因不偏風險估測之適應性除雜法
4-3 方法二:統計假設檢定法
4-3-1 含雜訊之信號
4-3-2 統計假設檢定除雜訊方法
4-4 方法三:空間相關性法
4-4-1 空間相關性步驟
4-4-2 方法分析
4-5 本章結論
第五章 模擬結果與比較
5-1 簡介
5-2 模擬含雜訊之信號測試
5-2-1 正常電力信號
5-2-2 電壓驟昇
5-2-3 電壓驟降
5-2-4 電壓暫態
5-2-5 持續性電力中斷
5-3 實際量測數值測試
5-3-1 實際量測信號一:電壓驟昇
5-3-2 實際量測信號二:電壓驟降
5-4 雜訊容忍度測試
5-5 本章結論
第六章 結論與未來研究方向
6-1 結論
6-2 未來研究方向
參考文獻
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