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研究生:劉又齊
研究生(外文):Yow-Chyi Liu
論文名稱:捷運系統軌道雜散電流與最大牽引功率降低之研究
論文名稱(外文):Reductions of Rail Stray Current and Peak Traction Power for MRT Systems
指導教授:林瑞禮陳建富陳建富引用關係
指導教授(外文):Ray-Lee LinJiann-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:2005
畢業學年度:93
語文別:英文
論文頁數:97
中文關鍵詞:捷運系統牽引電力雜散電流
外文關鍵詞:mass rapid transittrain-schedulingstray currentDC traction system
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  本論文提出一個新型的控制架構以降低捷運軌道雜散電流,以及使用遺傳演算法解列車排班最佳化以降低最大牽引電力之需求。
  早期捷運系統之軌道與大地之間沒有特別之絕緣設計,為直接接地系統,因其具有極高的雜散電流,後來改良成二極體接地系統及非接地系統。此二系統的確可降低雜散電流,但卻仍無法令人滿意。本論文提出一個新型的控制架構,可適用於二極體接地系統及非接地系統。控制牽引動力變電站之輸出電壓,使其維持定值,如此將能降低雜散電流。本文以高雄捷運為例,模擬多個牽引動力變電站及多列電車之系統。模擬的結果驗證本文所提的控制方法能大幅降低軌道電位及雜散電流。
  由於捷運列車電力需求曲線高低起伏相差很大之特性,加上若多列電車同時處於加速運轉行駛時,可能造成瞬間極高的牽引電力之需求。若能藉由排班技巧,避免多列電車同時處於加速運轉之狀態,將可降低最大牽引電力之需求。本文使用遺傳演算法解捷運排班之最佳化,模擬的結果驗證本文所提的方法可大幅降低最大牽引電力。
  This dissertation proposes a novel control scheme for reducing mass rapid transit (MRT) stray current, and applies genetic algorithms (GAs) for optimizing train-scheduling to reduce the maximum traction power.
  The earlier DC traction system is solidly grounded, with no specific insulation between the running rail and the earth. For reducing the magnitude of stray current, the diode-grounded system and the non-grounded system offer improvements over the solidly grounded system, but the amount of stray current remains large. This dissertation proposes a novel control scheme. The output voltage of the traction substation is controlled such that it remains constant for the diode-grounded system and the non-grounded system, thus the magnitude of rail potential and stray current can be reduced. This dissertation uses Kaohsiung MRT systems as an example to simulate multiple traction substations and multiple trains. The simulation results show that this novel control scheme can greatly lessen the stray current and rail potential.
  Because of the traction power load characteristics of MRT systems, a significant difference exists between the peak and valley of the traction power load curve. Furthermore, an extremely high traction power peak occurs if numerous trains are accelerating simultaneously. Careful train-scheduling can avoid the simultaneous acceleration of too many trains, and thus the maximum traction power can be reduced. This dissertation employs GAs to optimize train-scheduling. Simulation results show that the proposed method can significantly reduce the maximum traction power.
ABSTRACT ii
CONTENTS I
LIST OF TABLES IV
LIST OF FIGURES V
CHAPTER 1 INTRODUCTION 1
1.1 Study Backgrounds and Motivations 1
1.2 Study Methods and Steps 3
1.3 Main Contributions 4
1.4 Organization of the Dissertation 5
CHAPTER 2 INTRODUCTION OF THE MRT POWER SYSTEM 7
2.1 Introduction 7
2.2 Traction Substation 10
2.2.1 Rectifier Only 10
2.2.2 Rectifier and Inverter 12
2.3 The Kaohsiung MRT System 14
2.3.1 System Description 14
2.3.2 Bulk Substation 15
2.3.3 Traction Substation 16
2.3.4 Auxiliary Substation 18
2.3.5 Characteristics of the Train 19
2.3.6 Route Shape 20
2.4 Simulation Software 20
2.4.1 Train Performance Simulator 22
2.4.2 Electric Network Simulator 24
2.5 Simulation Results 25
2.5.1 Orange Line 25
2.5.2 Red Line 30
CHAPTER 3 STRAY CURRENT ANALYSIS 33
3.1 Introduction 33
3.2 Corrosion Theory 35
3.3 Grounded System 37
3.3.1 Solidly Grounded System 37
3.3.2 Diode-Grounded System 38
3.3.3 Non-Grounded System 39
3.4 Stray-Current Prevention 40
3.4.1 Cathodic Protection 40
3.4.2 Coatings 42
3.4.3 Drainage Method 42
3.4.4 Increasing the Insulating Resistance of the Rail and the Earth 43
3.4.5 Reducing the Resistance of the Running Rail 46
3.5 Stray-Current Model 46
3.5.1 Basic Stray-Current Model 46
3.5.2 Transmission-Line Analysis 48
3.5.3 Analysis of Multiple Substations and Trains 52
CHAPTER 4 STRAY CURRENT CONTROL 53
4.1 Introduction 53
4.2 The Diode-Grounded System 53
4.2.1 A Novel Control Scheme for Reducing Stray Current 53
4.2.2 Simulation Results 56
4.3 The Non-Ground Systems 63
4.3.1 A Novel Control Scheme for Reducing Stray Current 63
4.3.2 Simulation Results 65
4.4 Conclusions 68
CHAPTER 5 REDUCING MAXIMUM TRACTION POWER 69
5.1 Introduction 69
5.2 Genetic Algorithms 71
5.2.1 Encoding 71
5.2.2 Population Size 72
5.2.3 Fitness Function 72
5.2.4 Crossover 74
5.2.5 Mutation 76
5.2.6 Elitism 77
5.3 Simulation Results 77
5.3.1 Six-Minute Headway 79
5.3.2 Nine-Minute Headway 83
5.4 Conclusions 85
CHAPTER 6 CONCLUSIONS AND FUTURE STUDY 86
REFERENCES 88
LIST OF PAPERS 96
RESUME 97
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