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研究生:林鈺洋
研究生(外文):Yu-YangLin
論文名稱:基於卷積神經網路與資料融合技術之局部放電檢測系統
論文名稱(外文):Partial Discharge Detecting System Based on Convolution Neural Network and Data Fusion
指導教授:戴政祺
指導教授(外文):Cheng-Chi Tai
學位類別:碩士
校院名稱:國立成功大學
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:2020
畢業學年度:108
語文別:中文
論文頁數:65
中文關鍵詞:局部放電卷積神經網路資料融合影像辨識
外文關鍵詞:Partial DischargeConvolution Neural NetworkData FusionImage Recognition
相關次數:
  • 被引用被引用:2
  • 點閱點閱:180
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文以卷積神經網路與資料融合技術建構電力設備局部放電檢測系統。本文所提出的系統主要分成硬體部分及軟體部分,硬體部分中以高頻比流器(HFCT)、耦合電容(CC-TEV)和超高頻感測器(UHF)作為感測器量測電力設備之局部放電訊號,以NI PXI-5105高速擷取卡擷取量測訊號並傳輸至高頻寬嵌入式控制器(NI PXIe-8135);在軟體端使用LabVIEWTM執行訊號預處理與特徵圖譜繪製並建構使用者介面提供使用者相關資訊,設定系統蒐集50個電力週期的原始資料並繪製PRPD圖譜作為辨識局部放電類型的依據。系統中以卷積神經網路作為辨識模型分辨電力設備的局部放電類型,當PRPD圖譜繪製完成後使用特徵級資料融合技術結合不同感測器之訊息,提供辨識模型更完整的事件訊息,接著使用Python呼叫預先訓練好的卷積神經網路模型進行局部放電類型辨識,最終將卷積神經網路之判別結果回傳使用者介面,使用者可以透過使用者介面檢視判別結果趨勢變化,透過上述技術建構局部放電檢測系統,並透過常見的局部放電類型實驗資料進行系統的驗證。
This research mainly uses the convolutional neural network (CNN) and data fusion technology to diagnose the partial discharge (PD) type of power equipment. The system proposed in this paper is mainly divided into hardware and software parts. In hardware, we use NI PXI-5105 for data acquisition and transmit the PD signal of power equipment measured by sensors to the high-bandwidth embedded controller NI PXIe-8135. In software, LabVIEWTM is used to perform signal preprocessing, manufacture feature maps, and construct user interface to provide user information. We also use Python to implement the CNN algorithm. First of all, the discrete wavelet transform is used to filter the noise to get a noise-free PD signal. After the signal pre-processing, the system will manufacture phase resolved partial discharge (PRPD) patterns with 50 power cycle period as the input of the CNN. The system will use the CNN as the classification model to identify the PD of power equipment. In addition to CNN, we also adopt data fusion technology to improve the system performance. When the PRPD pattern is done, feature-level data fusion technology combines the information of different sensors to provide complete event information for the classification model. The pre-trained CNN model will be called by Python to identify the PD type. At last, Python returns the discrimination results to the user interface. The user can look over the discrimination results through the user interface. The system proposed in this paper is constructed by the technologies mentioned above and verified through the typical PD experiment.
摘要 I
Extended Abstract II
誌謝 X
目錄 XI
表目錄 XIV
圖目錄 XV
第一章 緒論 1
1.1 研究背景 1
1.2 國內外文獻回顧 2
1.3 研究動機與目的 4
1.4 論文大綱 6
第二章 局部放電類型及檢測方法 7
2.1 局部放電原理與類型 7
2.1.1 電暈放電 7
2.1.2 內部放電 8
2.1.3 表面放電 8
2.2 局部放電檢測方法 9
第三章 卷積神經網路與資料融合 11
3.1 卷積神經網路 11
3.2 資料融合 20
第四章 系統架構與實驗 24
4.1 系統架構 24
4.1.1 硬體架構 24
4.1.2 軟體設計 29
4.2 局部放電實驗及特徵擷取 33
4.2.1 電暈放電實驗 33
4.2.2 內部放電實驗 34
4.2.3 表面放電實驗 36
4.2.4 50個電力週期之PRPD繪製 39
4.3 辨識模型訓練及測試階段 42
4.3.1 高頻比流器 44
4.3.2 耦合電容感測器 48
4.3.3 超高頻感測器 51
4.3.4 結合卷積神經網路與資料融合技術之局部放電辨識 55
4.3.5 結果與討論 58
第五章 結論與未來研究方向 61
5.1 結論 61
5.2 未來研究方向 62
參考文獻 63
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