研究生: |
洪晨軒 Chen-Hsuan Hung |
---|---|
論文名稱: |
低壓屋內配線串聯電弧故障檢測與辨識 Detection and Recognition of Series Arc Fault on Low Voltage Indoor Distribution Lines |
指導教授: |
吳啟瑞
Chi-Jui Wu |
口試委員: |
李尚懿
San -Yi Lee 莊永松 Yung-Sung Chuang 郭明哲 Ming-Tse Kuo |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電機工程系 Department of Electrical Engineering |
論文出版年: | 2013 |
畢業學年度: | 101 |
語文別: | 中文 |
論文頁數: | 108 |
中文關鍵詞: | 火災 、低壓配線 、電弧故障檢測 、倒傳遞類神經網路 |
外文關鍵詞: | Fire, Low-Voltage Distribution Line, Arc-Fault Detection, Back-propagation Algorithm |
相關次數: | 點閱:655 下載:7 |
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現今的工廠與居家之低壓配電線路中都裝設過電流及過載保護裝置,有些特定支路還必須安裝漏電斷路器,來確保用電安全。然而,根據國外調查指出,有些住宅火災發生原因,懷疑是因為電弧故障所引起。電弧故障斷路器(Arc-Fault Circuit Interrupter-AFCI)是一種偵測線路中電弧故障發生的保護裝置,它透過對電弧特性判別來偵測串聯及並聯電弧,在電弧的熱能引起火災前盡早切離電源。本研究針對幾種不同特性的負載,進行串聯電弧故障檢測,經由電弧故障檢測平台蒐集串聯電弧實驗數據,將實驗數據與相關文獻進行電弧的時頻特性比對及驗證,作為電弧故障檢測法的依據。本研究採用兩種策略,分別是頻域特性綜合分析法與小波高頻能量累積法進行訊號處理,並將結果擷取特徵,結合倒傳遞類神經類網路辨識電弧故障。最後,將電弧故障辨識結果與商用AFCI的動作結果進行比較。而研究結果顯示,兩種辨識系統與商用AFCI相比,的確能判斷出發生串聯電弧故障,且誤動作的機率不高。本論文找出的檢測方法未來如能開發使用,應可在電弧的熱能引起火災前儘早切離電源,降低火災的發生率。
For the safe use of electric power, it is important to detect the occurring of line faults on the low voltage power circuits and switch off the power source before the occurring of fires by using protection devices, such as magnetic circuit breakers and leakage current detectors. However, many oversea examples reveal the facts that a number of home fires are caused by electric arc faults. The arc-fault circuit interrupter (AFCI) is a device which can detect the occurring of electric arc faults on the low voltage circuits, and then it can switch off the power source before the occurring of fire. In this study, it is to investigate series arc fault detection of circuits feeding several characteristics of the load. The test data are collected through the arc fault testing platform. The series arc fault will be verified by evaluating the time-frequency characteristics of experimental data and compared with relative literature. This study proposes two strategies for the signal processing of experimental data, and then building the extracted features based on the results. These extracted features are applied to artificial neural network (ANN) for recognition of arc fault. Finally, the experimental data with series arc faults are used to test the detecting methods and compare with the commercial devices. As the results of recognition, the purposed recognition systems can effectively detect the occurring of series arc faults, and the probability of malfunction is low.
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