近年來由於高科技產業與電腦設備等精密儀器發展過於迅速，相對的對於供電品質的要求也隨之增高，穩定的供電品質對於用戶端來說是非常重要的，因此，輸電線路上的故障類型與位置偵測，是電力系統中重要的研究項目。傳統電力系統故障分為三相平衡故障與不平衡故障，當電力系統發生三相平衡故障或不平衡故障時，如果能快速的辨別出故障種類和位置，電力公司則可以迅速的恢復供電，並減少停電時間以及停電時所產生的損失。

本論文利用Power World Simulator 13 建立台電345kV電力系統模型，使用支撐向量機來分類電力系統故障種類和位置，結合模糊理論中模糊規則庫與解模糊化後所計算出特徵值，可以使得分類更為明顯，利用此種技術可以使得故障種類與位置的判別準確度上升，從而提供快速、準確的故障判斷。

Recent years, due to high-tech industry and computer equipment developed too quickly, the requirements for power quality are increased. Stable power supply is very important for customer. Therefore, detecting the fault location and type on the transmission line is an essential part of research regarding power system. Traditional power system divided the fault into balance and unbalance. If type and location of power system fault can be identified sooner when it happens, power company can resume electricity supply at soonest and minimize the time and cost arising from the power outage.

In this thesis, using Power World Simulator13 to established the 345kV Taiwan power system model and Support Vector Machine to classify the fault type and location in power system. After combining the fuzzy rule base from fuzzy theorem and defuzzified values, it can make the classification of fault location and type more distinctly which further lead to quicker and more accurate judgment of the fault.

摘要 i
Abstract ii
目錄 iii
圖次 vi
表次 ix
第一章 緒論 1
1.1研究背景與動機 1
1.2文獻回顧 1
1.3本文貢獻 3
1.4章節概要 4
第二章 電力系統故障模型與問題描述 6
2.1前言 6
2.2問題描述 6
2.3差值正規化 7
2.4電力系統故障類型 8
2.4.1單線接地故障 9
2.4.2線對線故障 10
2.4.3三相接地故障 11
2.4.4雙線接地故障 11
第三章 模糊理論與支撐向量機 14
3.1模糊理論簡介 14
3.1.1模糊集合 15
3.1.2歸屬函數 18
3.1.3設計模糊邏輯控制器的規則 20
3.1.4模糊控制器輸出的解模糊化 20
3.2支撐向量機簡介 23
3.2.1分類與回歸 24
3.2.2核心函數 30
第四章 模糊系統設計與支撐向量機參數選取 32
4.1模糊系統架構 32
4.2模糊系統設計 33
4.2.1模糊系統歸屬函數 33
4.2.2模糊系統規則庫 36
4.2.3模糊系統測試結果 37
4.3決定最小平方支撐向量機參數 40
第五章 案例分析 43
5.1系統架構 43
5.2模擬四種故障類型 47
5.2.1單線接地故障 47
5.2.2線對線故障 50
5.2.3三相接地故障 52
5.2.4雙線接地故障 55
5.3模擬故障距離與種類 58
第六章 結論與未來研究方向 63
6.1結論 63
6.2未來研究方向 64
參考文獻 65
附錄A匯流排資料 69
附錄B線路資料 75

[1] Fang-Xing Li, Wei Qiao, Hong-Bin Sun, Hui Wan, Jian-Hui Wang, Yan Xia, Zhao Xu, and Pei Zhang, “Smart Transmission Grid: Vision and Framework,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 168-177, Sep. 2010.
[2] Whei-Min Lin, Chin-Der Yang, Jia-Hong Lin, and Ming-Tong Tsay, “A Fault Classification Method by RBF Neural Network With OLS Learning Procedure,” IEEE Transactions on Power Delivery, vol. 16, no. 4, pp. 473-477, Oct. 2001.
[3] Surya Santoso, W. Mack Grady, J. Edward, J. Lamoree, and Bhatt S. C., “Characterization of Distribution Power Quality Events with Fourier and Wavelet Transforms,” IEEE Transactions on Power Delivery, vol. 15, no. 1, pp. 247-254, Jan. 2000.
[4] G. T. Heydt, P. S. Fjeld, C. C. Liu, D. Pierce, L. Tu, and G. Hensley, “Applications of the Windowed FFT to Electric Power Quality Assessment,” IEEE Transactions on Power Delivery, vol. 14, no. 4, pp. 1411-1416, Oct. 1999.
[5] P. Pillay and A. Bhattacharjee, “Application of Wavelets to Model Short-Term Power System Disturbances,” IEEE Transactions on Power Systems, vol. 11, no. 4, pp. 2031-2037, Nov. 1996.
[6] A. M. Gaouda, M. M. A. Satama, M. R. Sultan, and A. Y. Chikhani, “Power Quality Detection and Classification Using Wavelet-Multiresolution Signal Decomposition,” IEEE Transactions on Power Delivery, vol. 14, no. 4, pp. 1469-1476, Oct. 1999.
[7] G. T. Heydt and A. W. Galli, “Transient Power Quality Problems Analyzed Using Wavelets,” IEEE Transactions on Power Delivery, vol. 12, no. 2, pp. 908-915, Apr. 1997.
[8] L. Angrisani, P. Daponte, M. D’Apuzzo, and A. Testa, “A Measurement Method Based on the Wavelet Transform for Power Quality Analysis,” IEEE Transactions on Power Delivery, vol. 13, no. 4, pp. 990-998, Oct. 1998.
[9] Omar A. S. Youssef, “A Modified Wavelet-Based Fault Classification Technique,” Electric Power Systems Research, vol. 64, Issue 2, pp. 165-172, Feb. 2003.
[10] Peilin L. Mao and Raj K. Aggarwal, “A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network,” IEEE Transactions on Power Delivery, vol. 16, no. 4, pp. 654-660, Oct. 2001.
[11] B. Perunicic, M. Mallini, Z. Wang, and Y. Liu, “Power Quality Disturbance Detection and Classification Using Wavelets and Artificial Neural Networks,” Proceeding 8th International Conference On Harmonics and Quality of Power Proceedings, vol. 1, no., pp. 77-82, Oct. 1998.
[12] 江瑞利，「以小波類神經網路為基礎之電力擾動辨識器」，中華民國第23屆電力工程研討會論文集，2002年。
[13] F. Mo and W. Kinsner, “Wavelet Modelling of Transients in Power Systems,” IEEE Conference on Communications, Power and Computing. WESCANEX 97 Proceedings, pp. 132-137, May. 1997.
[14] Surya Santoso, J. Edward, W. Mack Grady, and Antony C. Parsons, “Power Quality Disturbance Waveform Recognition Using Wavelet-Based Neural Classifier-Part I: Theoretical Foundation,” IEEE Transactions on Power Delivery, vol. 15, no. 1, pp. 222-228, Jan. 2000.
[15] Surya Santoso, J. Edward, W. Mack Grady, and Antony C. Parsons, “Power Quality Disturbance Waveform Recognition Using Wavelet-Based Neural Classifier II: Application,” IEEE Transactions on Power Delivery, vol. 15, no. 1, pp. 229-235, Jan. 2000.
[16] Wei-Rong Chen, Qing-Quan Qian, and Xiao-Ru Wang, “Wavelet Neural Network Based Transient Fault Signal Detection and Identification,” Information Communications and Signal Processing 1997. ICICS Proceedings of 1997 International Conference on vol.3, pp. 1377-1381, vol. 3, 9-12 Sep. 1997.
[17] 謝秉成，「智慧型電力品質事件辨識」，逢甲大學碩士論文，2002年。
[18] Surya Santoso, J. Edward, W. Mack Grady, and Peter Hofmann, “Power Quality Assessment Via Wavelet Transform Analysis,” IEEE Transactions on Power Delivery, vol. 11, no. 2, pp. 924-930, Apr. 1996.
[19] B. Perunicid, M. Mallini, Z. Wang, and Y. Liu, “Power Quality Disturbance Detection and Classification Using Wavelets and Artificial Neural Networks,” Harmonics and Quality of Power Proceedings, 1998. Proceedings. 8th International Conference On vol. 3, pp. 77-82 vol. 1, 14-18 Oct. 1998.
[20] T. Kohonen, “Self-Organizing Maps,” 3rd., New York, 2001.
[21] A. Eimitwally, S. Farghal, M. Kandil, S. Abdelkader, and M. Elkateb, “Proposed Wavelet-Neurofuzzy Combined System for Power Quality Violations Detection and Diagnosis,” IEE Proceedings-Generation Transmission and Distribution, vol. 148, no. 1, pp. 15-20, Jan. 2001.
[22] L. Angrisani, P. Daponte, M. D’Apuzzo, and A. Testa, “A Measurement Method Based on the Wavelet Transform for Power Quality Analysis,” IEEE Transactions on Power Delivery, vol. 13, no. 4, pp. 990-998, Oct. 1998.
[23] OM P. Gandhi, JOHN F Deford, and Hiroshi Kanai, “Impedence Method for Calculation of Power Deposition Patterns in Magnetically Induced Hyperthermia,” IEEE Transactions on Biomedical Engineering, vol.BME-31, no. 10, pp. 644-651, Oct. 1984.
[24] Hong-seok Song, Hyun-gyu Park, and Kwanghee Nam, “An Instantaneous Phase Angle Detection Algorithm under Unbalanced Line Voltage Condition,” IEEE 30th Annual Power Electronics Specialists Conference 1999. PESC 99, vol. 1, pp. 533-537, Aug. 1999.
[25] L. A. Zadeh, “Fuzzy Sets,” Information Control, vol. 8, pp. 339-353, 1965.
[26] L. A. Zadeh, “The Concept of A Linguistic Variable and its Application to Approximate Reasoning-I,” Information Sciences, vol. 8, no. 4, pp. 199-249, 1975.
[27] L. A. Zadeh, “The Concept of A Linguistic Variable and its Applications to Approximate Reasoning-II,” Information Sciences, vol. 8, no. 4, pp. 301-357, 1975.
[28] 孫宗瀛、楊英魁，「Fuzzy 控制理論、實作與應用」，全華科技圖書股份有限公司，2001年。
[29] 黃品勳，「應用模糊控制理論於太陽能儲能系統之研製」，國立中山大學電機工程研究所碩士論文，2005年。
[30] Corinna Cortes and Vladimir Vapnik, “Support-Vector Networks,” Mach. Learn.20, no. 3, pp. 273-297, Sep. 1995.
[31] Nello Cristianini and John Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods,” Cambridge University Press, Cambridge, UK, Mar. 2000.
[32] B. Schölkopf, A. J. Smola, R. Willianson and P. Bartlett, “New support vector algorithms,” Neural Comput. vol. 12, no. 5, pp. 207-1245, 2000.
[33] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining Knowledge. vol. 2, no. 2, pp .1-47, 1998.
[34] 吳建賢，「軟計算於電力品質偵測與電機故障診斷之應用」，國立中山大學電機工程研究所博士論文，2008年。
[35] Whei-Min Lin, Chien-Hsien Wu, Chia-Hung Lin, and Fu-Sheng Cheng, “Classification of Multiple Power Quality Disturbances Using Support Vector Machine and One-versus-One Approach,” International Conference on Power System Technology, pp .1-8, 22-26 Oct. 2006.
[36] 林義隆，「基於支撐向量機之細胞神經網路韌性模板設計及Wilcoxon學習機之初步研究」，國立中山大學電機工程研究所博士論文，2006年。
[37] H. T. Lin and C. J. Lin, “A Study on Sigmoid Kernels for SVM and the Training of Non-PSD Kernels by SMO-Type Methods,” Technical report, Department of Computer Science & Information Engineering, National Taiwan University, 2003.
[38] Bas J. de Kruif and Theo J. A. de Vries, “Pruning Error Minimization in Least Squares Support Vector Machines,” IEEE Transactions on Neural Networks, vol. 14, no. 3, pp. 696-702, May. 2003.
[39] Tony Van Gestel, Johan A. K. Suykens, Dirk-Emma Baestaens, Annemie Lambrechts, Gert Lanckriet, Bruno Vandaele, Bart De Moor, and Joos Vandewalle, “Financial Time Series Prediction Using Least Squares Support Vector Machines Within the Evidence Framework,” IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 809-821, Jul. 2001.
[40] B. Schölkopf and A. J. Smola, “Learning with Kernel Cambridge,” MA: MIT Press, 2001.
[41] M. A. Aizerman, E. A. Braverman, and L. Rozonoer. “Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning,” Automation and Remote Control, 25:821-837, 1964.