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研究生:吳建賢
研究生(外文):Chien-Hsien Wu
論文名稱:軟計算於電力品質偵測與電機故障診斷之應用
論文名稱(外文):Applications of Soft Computing for Power-Quality Detection and Electric Machinery Fault Diagnosis
指導教授:林惠民林惠民引用關係
指導教授(外文):Whei-Min Lin
學位類別:博士
校院名稱:國立中山大學
系所名稱:電機工程學系研究所
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:2008
畢業學年度:97
語文別:英文
論文頁數:160
中文關鍵詞:軟計算電力品質干擾汽輪發電機故障診斷感應機故障診斷支撐向量機灰聚類分析機率神經網路
外文關鍵詞:Soft Computing (SC)and Probabilistic Neural Network (PNN)Power-Quality Disturbances (PQD)Induction Motor Fault Diagnosis (IMFD)Support Vector Machine (SVM)Turbine-Generator Fault Diagnosis (TGFD)Grey Clustering Analysis (GCA)
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隨著電業自由化與市場競爭的來臨,電力供應之穩定度與可靠性,是獨立系統業者(Independent System Operator, ISO)之重要關注課題。未來電力品質之研究將更為重要;諧波、電壓陡升、電壓驟降、及電力中斷等將降低供電品質。最近高速鐵路與大眾運輸系統快速發展,隨著廣泛的半導體技術於自動牽引系統之應用,由於這些電子設備和非線性負載的高度使用造成諧波失真水準惡化,為了確保電力品質,電力干擾偵測變得重要,而有分類能力的偵側方法將有助於偵測干擾位置和類型。
電機故障診斷是電力公司和用戶相當注意的另一個議題。獨立系統業者需要提供高品質的服務以留住客戶。汽輪發電機的故障診斷對電廠利益具有巨大影響。發電機故障不僅損壞發電機本身,而且引起停機和利潤的損失。由於高溫,高壓與諸如熱疲勞之種種因素,很多組成部分可能出錯,如此不僅將造成巨大經濟損失,有時甚至會影響社會安全。因此,發現發電機故障並且採取立即措施以避免損失是必要的。此外,感應機於電力系統中扮演主要角色。為節省費用,能於定期檢查發現電動機潛在的故障極為重要。早期發現的預防技術能查明潛在故障和避免停機。本論文發展各種應用於偵測電力品質干擾(Power-Quality Disturbances, PQD)、汽輪發電機故障診斷(Turbine-Generator Fault Diagnosis, TGFD)和感應機故障診斷(Induction Motor Fault Diagnosis, IMFD)的軟計算(Soft Computing, SC)演算法。所提出的軟計算方法包括支撐向量機(Support Vector Machine, SVM),灰聚類分析(Grey Clustering Analysis, GCA),以及機率神經網路(Probabilistic Neural network, PNN)。整合所提出之診斷程序與既有的監控系統,期望在不須增加任何設備下,建構完整之電力監控系統。最後並以合理而實際的方式來評估本文所提出的方法。與傳統方法相比較,測驗結果顯示,本文所提出的演算法具有高準確度,強韌性佳和快速處理之性能。
With the deregulation of power industry and the market competition, stable and reliable power supply is a major concern of the independent system operator (ISO). Power-quality (PQ) study has become a more and more important subject lately. Harmonics, voltage swell, voltage sag, and power interruption could downgrade the service quality. In recent years, high speed railway (HSR) and massive rapid transit (MRT) system have been rapidly developed, with the applications of widespread semiconductor technologies in the auto-traction system. The harmonic distortion level worsens due to these increased uses of electronic equipment and non-linear loads. To ensure the PQ, power-quality disturbances (PQD) detection becomes important. A detection method with classification capability will be helpful for detecting disturbance locations and types.
Electric machinery fault diagnosis is another issue of considerable attentions from utilities and customers. ISO need to provide a high quality service to retain their customers. Fault diagnosis of turbine-generator has a great effect on the benefit of power plants. The generator fault not only damages the generator itself, but also causes outages and loss of profits. With high-temperature, high-pressure and factors such as thermal fatigues, many components may go wrong, which will not only lead to great economic loss, but sometimes a threat to social security. Therefore, it is necessary to detect generator faults and take immediate actions to cut the loss. Besides, induction motor plays a major role in a power system. For saving cost, it is important to run periodical inspections to detect incipient faults inside the motor. Preventive techniques for early detection can find out the incipient faults and avoid outages. This dissertation developed various soft computing (SC) algorithms for detection including power-quality disturbances (PQD), turbine-generator fault diagnosis, and induction motor fault diagnosis. The proposed SC algorithms included support vector machine (SVM), grey clustering analysis (GCA), and probabilistic neural network (PNN). Integrating the proposed diagnostic procedure and existing monitoring instruments, a well-monitored power system will be constructed without extra devices. Finally, all the methods in the dissertation give reasonable and practical estimation method. Compared with conventional method, the test results showed a high accuracy, good robustness, and a faster processing performance.
CONTENTS

ACKNOWLEDGEMENT
ABSTRACT (in Chinese)...….…………………………………………..... I
ABSTRACT (in English)…..…………………………………………....... III
CONTENTS…………………………………………………………………..………... V
LIST OF FIGURES……….………………………………………………. VIII
LIST OF TABLES………………………………………………………... X

CHAPTER 1 INTRODUCTION ……………………………………………... 1
1.1 Motivation and Background…………………………………... 1
1.2 Brief Sketch of the Contents………………………………….. 3

CHAPTER 2 RESEARCH METHODS…………………….…………. 5
2.1 Support vector machine (SVM) ………………………………. 5
2.2 Grey clustering analysis (GCA) ………………….................... 21
2.3 Probabilistic Neural Network (PNN) …………………………. 25

CHAPTER 3 THE ARCHITECTURE OF POWER QUALITY DISTURBANCES DETECTION SYSTEM……………...
30
3.1 Preface………………………………………………............... 30
3.2 Proposed Design Architecture………………………………… 32
3.3 Summary……………………………………………………… 39

CHAPTER 4 THE ARCHITECTURE OF TURBINE-GENERATOR FAULT DIAGONOSIS SYSTEM……………………………
40
4.1 Preface…………………………………………………….….. 40
4.2 Problem Description………………………………………….. 42
4.3 The Proposed GCA Based Classifier…………………............ 46
4.4 The Proposed BSVR Based Classifier……………….............. 55
4.5 Summary…..…………………………………………………. 59

CHAPTER 5 THE ARCHITECTURE OF INDUCTION MOTOR FAULT DIAGNOSIS SYSTEM……………………………... 60
5.1 Preface…..………………………………………..………….. 60
5.2 The Influence of Loads and Faults on Spectrum………........... 62
5.3 Band Drift Processing….……. …….…….…….…………….. 63
5.4 Amplitude Variations Processing….…….…….……………… 71
5.5 Sound-Signals Based Fault Diagnosis System (SFDS) ……….. 75
5.6 Summary…….…….…………………….................................... 79

CHAPTER 6 SIMULATION RESULTS AND DISCUSSION………...... 80
6.1 Simulation Tests of the proposed DEDS……………………… 80
6.2 Simulation Tests of the proposed GFDP………………........... 96
6.3 Simulation Tests of the proposed BGFDS……………………. 104
6.4 Simulation Tests of the proposed SFDS……………………… 114

CHAPTER 7 CONCLUSION AND FUTURE RESEARCH…………….. 129
7.1 Conclusion……………………………………………………. 129
7-2 Future Research………………………………………………. 133

REFERENCES……………………………………………………………… 135
LIST OF PAPERS AND PROJECTS………………………………………. 142
BIOGRAPHY……………………………………………………………….. 146
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