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研究生： Suleyman GUVEN
Suleyman - GUVEN
論文名稱： 應用調適性神經模糊推論系統於氣體絕緣開關之局部放電圖譜分類研究
Partial Discharges Pattern Classification in GIS Using Adaptive Neuro Fuzzy Inference System
指導教授： 吳瑞南
Ruay-Nan Wu
口試委員: 張宏展
Hong-Chan Chang
陳建富
Jiann-Fuh Chen
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： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 77
中文關鍵詞： 局部放電 、氣體絕緣開關 、調適性神經模糊推論系統 、圖形辨識 、線性判別分析
外文關鍵詞： partial discharge, gas insulted switchgear, ANFIS, pattern recognition, LDA
相關次數： 點閱：329  下載：9 
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    • 摘要
    在高壓設備的絕緣系統診斷方法中，局部放電量測為其中最重要的部分，利用它可方便地評估絕緣狀態及其潛在的條件。局部放電活動可能來自於各種瑕疵，並發生相對應不同的行為。在本文，於實驗室針對三個不同的氣體絕緣開關瑕疵所產生的PD模型進行記錄與分析。這項研究旨在進行含有預設瑕疵的GIS設備之PD測試。採用統計學方法針對局部放電資料進行特徵萃取，並利用線性判別分析(LDA)來選擇所要使用的特徵值。運用調適性神經模糊推論系統(ANFIS)來進行訓練。訓練完成後用來辨識局部放電資料的來源。ANFIS的分類結果表示出較高的成功率，並且最高的平均成功率已達到95.83%

    ABSTRACT

    Partial discharge (PD) measurement is among the most important diagnostics methods of insulation systems in high voltage equipment, which makes it convenient to assess the insulation status. Partial discharge activities may stem from various defects, and correspondingly behave differently. Here, the PD patterns produced by 3 different laboratory models representing defects in GIS are recorded and analyzed. The research aimed at conducting PD tests with three GIS apparatus including prefabricated defects. From the PD pattern data, statistical features were extracted and these features were reduced by linear discriminant Analysis (LDA). Adaptive neuro-fuzzy inference system (ANFIS) was used to train the fuzzy inference system (FIS). The trained FIS was then used to recognize the source of the PDs. Results show that ANFIS classification has a high success rate and highest average success rate at 38kV reaches 95.83%.

    TABLE OF CONTENTS 摘要 I ABSTRACT II ACKNOWLEDGMENTS III TABLE OF CONTENTS IV LIST OF FIGURES VII CHAPTER 1 INTRODUCTION 1.1 Project Background 1 1.2 Significance of Research 2 1.3 Objectives 2 1.4 Scope of Work and Methodology 3 1.5 Thesis Outline 4 CHAPTER 2 PARTIAL DISCHARGE MEASUREMENT SYSTEM AND EXPERIMENTAL SETUP 2.1 Partial discharge introduction 6 2.1.1 Partial discharge definition 7 2.1.2 Classification of partial discharge 9 2.1.3 Effect of a partial discharge in insulating system 10 2.1.4 PD detection methods 11 2.2 Partial discharge in GIS 13 2.2.1 GIS Introduction 13 2.2.2 GIS in power system 13 2.2.3 PD propagation in GIS 15 2.2.4 PD types in GIS 15 2.3 The implementation and application of PD monitoring system on GIS 16 2.3.1 GIS experiment objective 16 2.3.2 GIS defect model 16 2.3.3 PD Experimental setup 18 2.3.4 Experimental setup test procedures 22 CHAPTER 3 PARTIAL DISCHARGE DATA ACQUISITION AND ANALYSIS 3.1 Introduction 23 3.2 Partial Discharge Data Acquisition 24 3.3 Feature Extraction 29 3.3.1 Mean 31 3.3.2 Standard Deviation 33 3.3.3 Skewness (SK) 34 3.3.4. Kurtois (KU) 36 3.3.5 The discharge phase region (DPR) 38 CHAPTER 4 DIMENSIONALITY REDUCTION 4.1 Introduction 41 4.2 Techniques for dimensionality reduction 42 4.3 Feature extraction using Linear Discriminant Analysis 42 4.4 Computing LDA 44 CHAPTER 5 ANFIS MODELING AND SOFTWARE IMPLEMENTATION 5.1 Neuro-Fuzzy Model 49 5.2 Adaptive Neuro-Fuzzy inference system (ANFIS) 50 5.2.1 ANFIS Architecture 51 5.2.2 Learning algorithm of ANFIS 55 5.2.3 ANFIS Classifier 57 5.3 ANFIS Implementation in Classifying PD Defects 58 5.3.1 ANFIS Dataset 60 5.3.2 Training and Testing Datasets 61 5.3.3 Classification Performance 62 CHAPTER 6 RESULT AND DISCUSSION 6.1 Overview 64 6.2 Results 64 6.3 Performance Analysis 70 CHAPTER 7 CONCLUSION AND FUTURE WORK 7.1 Conclusion 72 7.2 Recommendations for Future Work 73 REFERENCES 74 AUTOBIOGRAPHY ……………………………………………………………… 77

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