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研究生:蘇明守
研究生(外文):Ming-Shou Su
論文名稱:電力電纜系統局部放電檢測與定位之研究
論文名稱(外文):A Study of Partial Discharge Detection and Allocation on Power Cable System
指導教授:陳建富陳建富引用關係
指導教授(外文):Jiann-Fuh Chen
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
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:2012
畢業學年度:100
語文別:英文
論文頁數:100
中文關鍵詞:局部放電高頻比流器模糊推論系統機率神經網路離散小波轉換
外文關鍵詞:partial discharge (PD)high frequency current transformer (HFCT)fuzzy inference system (FIS)probabilistic neural networks (PNN)discrete wavelet transform (DWT)
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本論文提出發生於電力電纜系統之局部放電(Partial discharge)檢測與定位之研究,並以高頻等效電路為理論基礎,建構三相輸電線之等效電路模式,用來模擬與判定在三相輸電中,局部放電發生在其中的那一相。局部放電之信號會沿著電力電纜傳遞,可以利用高頻比流器(High frequency current transformer)來量測與診斷局部放電是來自三相輸電線之那一相。由於使用傳統示波器進行量測時,容易受到輸電電纜與接地線之導體間的電容效應影響而造成誤判。因此,為準確判定局部放電源的位置,可以觀察三相輸電線同時量測的波形,並從波形的極性關係來判定局部放電源;透過高頻等效電路之模式的模擬與實際量測的結果,來獲得理論上的驗證。在三相的輸電線中,發生局部放電的那一相與其他兩相的波形是呈現反極性的現象,並且可以判定其屬於內部的局部放電。
本論文亦可應用在三相輸電線所連接之高壓設備,當其發生局部放電時,經由輸電電纜到高頻比流器所量測的信號之極性、大小與功率進行評估與整合。應用小波理論、模糊推論系統(Fuzzy inference system)與機率神經網路(Probabilistic neural networks)之專家系統,建置一套完整的量測分析程序,用來判定局部放電發生的那一相的準則。其中離散小波轉換(Discrete wavelet transform)是被應用於在從高頻比流器所量測信號之雜訊的抑制;白色高斯雜訊(White Gaussian noise)則是被使用加入局部放電信號中,用來模擬為高雜訊的量測環境。當這些信號濾除雜訊後,再根據克希何夫電流律(Kirchhoff’s current law),可以從信號的極性來確認是否為內部的局部放電。同時,基於功率傳輸的觀念,局部放電信號的絕對峰值與平均功率兩者被使用在這個量測的程序中,作為模糊推論系統與機率神經網路的輸入的變數,能夠有效改善傳統示波器測量診斷局部放電,同時也提昇局部放電發生預測的準確率;最後,從三相輸電系統與單一條電力電纜實測的結果,能夠驗證本論文所提出的量測分析程序之可行性。

This dissertation presents the studies of the detection and allocation of partial discharge (PD) in power cable system. Phase determination of PD source in three-phase transmission lines are based on high frequency equivalent circuit model. Since the PD signals are propagated as a travelling wave along the power cables, the high frequency current transformers (HFCT) are used to detect electricity signals and determine where PD sources take place in the transmission lines. The conventional observations of PD signals via oscilloscopes are affected by capacitive effect. Measuring the polarity of the waveform can improve the accuracy of PD source determination which is identified by an equivalent circuit model. From simulation and measurement results, the polarity of the waveform in the phase where PD occurs is just the inverse of the signal waveforms in other two phases. Detection of the polarity of PD waveform is adopted to identify internal PD signals. Furthermore, the crest value of the waveform, estimation of the power spectral density (PSD) and average power are used to verify this new approach.
This dissertation also proposes an approach to determine the phase where PD occurs in three-phase transmission lines via polarity, amplitude and power of PD signals assessment using expert systems of fuzzy inference system (FIS) and probabilistic neural networks (PNN). This method is also applied to the measurement in the three-phase power cables, which are connected to the high voltage equipments. Discrete wavelet transform (DWT) is employed to suppress noises of measured signals by HFCT. According to Kirchhoff’s current law (KCL), the polarity of the de-noised signal is applied to identify the PD signals in three-phase transmission lines. The white Gaussian noise, which simulates the high noise environment, are added to the PD signals, when making measurements. Base on the concept of power delivery, both the peak absolute value and average power of PD signals are adopted as input variables of the FIS and PNN. The accurate ratios of the PD occurrence prediction using conventional observations of PD signals via oscilloscopes are much improved by the proposed method. Moreover, this approach also successfully applied to identify the PD location in a power cable. Finally, the experimental results validate that the proposed approach, which can precisely determine the phase location of PD sources in the field testing and the exact position on the phase where PD occurs in practical PD measurement.

LIST OF CONTENTS I
LIST OF FIGURES IV
LIST OF TABLES IX
LIST OF ABBREVIATIONS X
LIST OF SYMBOLS XI
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Literature survey 2
1.3 Review of transmission-line modeling 5
1.4 Contribution of this dissertation 6
1.5 Organization of this dissertation 7
CHAPTER 2 HIGH FREQUENCY EQUIVALENT CIRCUIT MODEL 10
2.1 Introduction 10
2.2 Experimental setup 11
2.2.1 High frequency equivalent circuit of the power cable 11
2.2.2 Equivalent circuit of the detection probe 13
2.2.3 Simulation of PD source 13
2.3 Case study 15
2.3.1 Neglected capacitive effect 16
2.3.1.1 Case 1 16
2.3.1.2 Case 2 17
2.3.1.3 Case 3 17
2.3.2 Consideration of the capacitive effect 20
2.3.3 Estimate of the power spectral density 23
2.3.4 Field PD signals testing 29
2.4 Summary 33
CHAPTER 3 PHASE DETERMINATION OF PD SOURCE IN
THREE-PHASE TRANSMISSION LINES 34
3.1 Introduction 34
3.2 Experimental setup and data acquisition 35
3.2.1 Experimental setup 35
3.2.2 Discrete wavelet transform 40
3.2.2.1 Optimal wavelet selection 42
3.2.2.2 Automatic thresholding rule 44
3.2.2.3 Proposed de-noising procedure 45
3.2.3 Polarity of PD signals 45
3.3 Phase location of PD source 46
3.3.1 Design of FIS 46
3.3.2 Design of PNN 48
3.4 Results and discussion 54
3.4.1 Experimental results 54
3.4.2 Field PD signals testing 61
3.5 Summary 63
CHAPTER 4 IDENTIFICATION OF PD LOCATION IN A POWER CABLE 64
4.1 Introduction 64
4.2 Experimental setup and data acquisition 65
4.2.1 Discrete wavelet transform 65
4.2.2 Proposed de-noising procedure 67
4.2.3 Experimental setup 67
4.3 FIS and PNN 72
4.3.1 Design of FIS 72
4.3.2. Design of PNN 74
4.3.3 Proposed process for location of PD sources 76
4.4 Results and discussion 77
4.4.1 Experiment results 77
4.4.2 Practical PD measurement 84
CHAPTER 5 CONCLUSIONS AND FUTURE WORKS 87
5.1 Conclusions 87
5.2 Future works 88
REFERENCE 89
LIST OF PAPERS 98
VITA 100

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