電業自由化與市場開放是世界潮流趨勢，全球已有很多國家實施，台灣正在研擬電業法修正案，逐步推行電業自由化提昇電業經營效率。而電業自由化推行成功與否，繫於市場運作機制架構設計是否良好以及各種配套措施。台灣電力網路為一封閉的系統，且電力資源供需南北不平衡，因此電力網路的可靠性關係到電力市場的電力調度能否落實。電力公司面對自由化，應提昇其服務品質與競爭力，配電事故為影響電力系統安全性、可靠性以及供電品質的重要因素，為提昇供電品質，如何發展一個可以快速找到事故發生地點的方法，當事故發生時能快速發現事故位置，以及找到造成事故的損壞設備，以縮短故障排除所需時間，進而提昇供電系統的可靠性與品質，即成為電力公司所關心的議題。
本文目的為探討電業自由化下，一方面分析電業之市場競爭情形，針對發電市場競爭機制，情境模擬發電市場競爭時可能發生的狀況，討論目前所擬的競爭機制可能面對的問題，透過市場規則的訂定與配套措施的設計予以解決，作為相關單位決策之依據。另一方面，電業為提昇其供電服務品質與競爭力，針對配電事故定位診斷，建立配電事故診斷資料挖礦架構，其中包括約略集合方法資料挖礦模式，與關聯規則方法資料挖礦模式，以挖掘事故診斷記錄的共同特性，推導配電事故的損壞設備與事故現場資料之間的關聯規則，利用台電公司配電事故停電記錄表所記錄之資料為實證，以檢驗研究效度，並比較不同模式方法之差異以及優缺點，研究發現可提供電力公司維修人員在特定決策環境下，推測配電事故原因的規則，來減少事故定位所需的時間，增加事故診斷正確性，進而減少停電損失，提昇供電品質。

Deregulation of electric power industry has become a worldwide trend, undergoing in many countries including Taiwan. Because of the liberalization policy of Taiwan’s power industry, there is a critical demand to explore the mechanism of power market and thus design appropriate market structure to maintain the stability of market in a deregulated environment. However, given the characteristics of the Taiwanese power industry, specifically limited transmission capability and insufficient stability, the transmission capacity constraints are important for operating and planning power systems. Furthermore, distribution feeder fault causes power outages and will also significantly affect power systems’ reliability, security and quality, among other important factors. Therefore, it is important to apply useful methods to diagnose and thus locate the fault quickly to reduce the outage duration and avoid huge economical loss. In practice, feeder patrols in Taiwan Power Company (Taipower) usually identify the fault locations by referencing the regional distribution of the trouble calls, the abnormal observations of the feeders, and the observed conditions in the surrounding environments. Then, feeder patrols have to rush into the field along feeder to locate the fault mainly by visual inspection and by trial energization of the feeder, section by section. Such a trial feeder energization is harmful to the cable and frequently takes a long time for power restoration.
This dissertation aims to explore the mechanism for power market by using scenario analysis for examining the locational prices with load variation and different bidding strategies and to thus propose the appropriate operation guides for avoiding possible failure of market operation in a deregulated electric power market. This dissertation discusses critical success factors of the proposed market mechanism. In addition, this dissertation also aims to develop a data mining framework based on rough set theory and association rule to derive useful patterns and rules for distribution feeder fault equipment diagnosis for fault location. In particular, the historical data of distribution feeder faults, which occurred within the business area of Taipei City District Office of Taipower was used for validation. The results have demonstrated practical viability of data mining approach for fault location based on historical data. The feeder patrols can locate the fault location and find the fault equipment quickly through the derived inference rules.

中時晚報（1999），焦點新聞-竹科損失可能達百億，1999年7月30日。
中華資訊網（2003），紐約市損失近七億五千萬美元，2003年8月17日。
台灣電力公司網站（2004），http://www.taipower.com.tw。
行政院主計處網站（2001），http://www.dgbasey.gov.tw。
朱文成（2000），「電業架構與自由化之問題探討」，邁向電力新紀元電業自由化的展望國際研討會論文集，台灣經濟研究院與台灣電力公司，台北。
李振昌（1999），16定位，台北：大塊文化，譯自Papows, J., (1999), Enterprise.com。
李清榮（2000），「從制度設計觀點看電業法修正應有之配套措施」，新世紀台灣電業自由化論壇，中華經濟研究院，台北。
李清榮（2002），「自由化下電力市場交易制度與競爭機制」，電業自由化研討會論文集，經濟部能源委員會與台灣經濟研究院，台北。
林益裕（2000），「電業自由化與公平競爭」，新世紀台灣電業自由化論壇，中華經濟研究院，台北。
洪德生、黃義協、陳士麟、簡禎富、洪穎怡、楊宏澤、林章平、盧豐彰、張文恭等（2001），台灣電業自由化下發電市場競爭機制之研究，台灣經濟研究院與台電公司綜合研究所合作研究，台灣電力股份有限公司八十九年度研究計畫：531-89-3204。
許志義（2000），「國際電業自由化發展趨勢及其對台灣的涵義」，邁向電力新紀元電業自由化的展望國際研討會論文集，台灣經濟研究院與台灣電力公司，台北。
陳家仁、陳彥良、陳禹辰（2003），「在少樣商品或短交易長度情況下挖掘關聯規則」，資訊管理學報，第9卷，第2期，頁55-72。
梁啟源（2000），「他山之石-國外電業自由化之經驗」，邁向電力新紀元電業自由化的展望國際研討會論文集，台灣經濟研究院與台灣電力公司，台北。
張玉山、陳麗芬（1999），「我國電力自由化方案與電力交易制度之探討」，中華民國科技管理研討會論文集第三集，頁201-212。
彭金堂、簡禎富、陳士麟、林章平（2001），「電業自由化下模擬發電競爭與負載變動對發電競爭市場之影響」，中華決策科學年會暨論文研討會論文集，頁383-394。
彭金堂、簡禎富、陳士麟、林怡傑（2004），「以SWOT分析探討我國電力市場交易機制與配套措施」，管理創新與科際整合學術研討會論文集，頁33-53。
葉忠、吳恆睿（2002），「中醫院揀藥作業儲位規劃之研究」，運籌研究期刊，第2期，頁39-84。
楊景晴（2004），整合決策樹與關聯規則之資料挖礦架構及其實證研究，國立清華大學工業工程與工程管理研究所碩士論文。
經濟部能源委員會（2002），能源政策白皮書。http://www.moeaboe.gov.tw/02/01/energy/Index.htm。
簡禎富、林則孟、彭金堂、吳淑芬（2000），「服務系統服務品質滿意度之研究--以某大學圖書館為實證研究」，工業工程學刊，第17卷，第1期，頁1-13。
簡禎富、林鼎浩、徐紹鐘、彭誠湧（2001），「建構半導體晶圓允收測試資料挖礦架構及其實證研究」，工業工程學刊，第18卷，第4期，頁37-48。
蘇金勝（2000），「國外電業自由化之經驗及成果」，邁向電力新紀元電業自由化的展望國際研討會論文集，台灣經濟研究院與台灣電力公司，台北。
蘇金勝（2002），「我國電業自由化政策與電業法修正方向」，電業自由化研討會論文集，經濟部能源委員會與台灣經濟研究院，台北。
Agrawal, R., T. Imielinski and A. Swami, (1993), “Mining Association Rules Between Sets of Items in Large Databases,” Proceedings of The 1993 ACM SIGMOD International Conference on Management of Data, Washington DC, USA.
An, J. and Z. Guo, (2002), “Applications of Data Mining to E-commerce,” IEE563 Distributed Information Systems Research Report.
Anderson, D. R., D. J. Sweeney and T. A. Williams, (2002), Statistics for Business and Economics, 8th ed., Thomson Learning.
Berry, M. and G. Linoff, (1997), Data Mining Techniques for Marketing, Sales and Customer Support, John Wiley and Sons, New York.
Berzal, F., J. Cubero, N. Mann and J. Serrano, (2001), “TBAR: An Efficient Method for Association Rule Mining in Relational Databases,” Data and Knowledge Engineering, Vol. 37, pp. 47-64.
Beynon, M., B. Curry and P. Morgan, (2001), “Knowledge Discovery in Marketing: An Approach Through Rough Set Theory,” Knowledge Discovery in Marketing, pp. 915-935.
Beynon, M. J., (2004), “Stability of Continuous Value Discretisation: An Application within Rough Set Theory,” International Journal of Approximate Reasoning, Vol. 35, pp. 29-53.
Brachman, R. J., T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro and E. Simoudis, (1996), “Mining Business Databases,” Communication of The ACM, Vol. 39, Issue 11, pp. 42-48.
Cabena, P., P. Hadjinian, R. Stadler, J. Verhess and A. Zanasi, (1997), Discovering Data Mining From Concept to Implementatation, Prentice Hall PTR, Upper Saddle River, New Jersey.
Cardozo, E. and S. N. Talukdar, (1988), “A Distributed Expert System for Fault Diagnosis,” IEEE Transactions on Power Systems, Vol. 3, No. 2, pp. 641-646.
Changchien, S. W. and T. C. Lu, (2001), “Mining Association Rules Procedure to Support On-Line Recommendation by Customers and Products Fragmentation,” Expert Systems with Applications, Vol. 20, pp. 325-335.
Chen, M. C., (2003), “Configuration of Cellular Manufacturing Systems Using Association Rule Induction,” International Journal of Production Research, Vol. 41, No. 2, pp. 381–395.
Chien, C. F., S. L. Chen and Y. S. Lin, (2002), “Using Bayesian Network for Fault Location on Distribution Feeder,” IEEE Transactions on Power Delivery, Vol. 17, No. 13, pp. 785-793.
Chien, C. F., F. Y. Lo and J. T. Lin, (forthcoming), “Using DEA to Measure The Relative Efficiency of The Service Center and Improve Operation Efficiency Through Reorganization,” IEEE Transactions on Power System.
Chow, M. Y. and L. S. Taylor, (1993), “A Novel Approach for Distribution Fault Analysis,” IEEE Transactions on Power Delivery, Vol. 8, No. 4, pp. 1882-1889.
Dimitras, A. I., R. Slowinski, R. Susmaga and C. Zopounidis, (1999), “Business Failure Prediction Using Rough Sets,” European Journal of Operational Research, Vol. 114, Issue 2, pp. 263-280.
Duda, R., J. Gaschning and P. Hart, (1979), “Model Design in The Prospector Consultant System for Mineral Exploration,” in: Michie, D. (Eds.), Expert Systems in The Microelectronic Age, Edinburgh: Edinburgh University Press, pp. 153-167.
Duntsch, I. and G. Gediga, (1997), “Statistical Evaluation of Rough Set Dependency Analysis,” International Journal of Human-Computer Studies, Vol. 46, pp. 589-604.
Fayyad, U. M. and K. B. Irani, (1992), “On The Handling of Continuous-Valued Attributes in Decision Tree Generation,” Machine Learning, Vol. 8, Issue 1, pp. 87-102.
Fayyad, U., G. Piatetsky-Shapiro and P. Smyth, (1996), “The KDD Process for Extracting Useful Knowledge from Volumes of Data,” Communication of The ACM, Vol. 39, Issue 11, pp. 27-34.
Fukui, C. and J. Kawakami, (1986), “An Expert System for Fault Section Estimation Using Information from Protective Relays and Circuit Breakers,” IEEE Transactions on Power Delivery, Vol. 1, No. 4, pp. 83-90.
Girgis, A. A. and M. B. Johns, (1989), “A Hybrid Expert System for Faulted Section Identification, Fault Type Classification and Selection of Fault Location Algorithms,” IEEE Transactions on Power Delivery, Vol. 4, No. 2, pp. 978-985.
Girgis, A. A., C. M. Fallon and D. L. Lubkeman, (1993), “A Fault Location Technique for Rural Distribution Feeders,” IEEE Transactions on Industry Applications, Vol. 29, No. 6, pp. 1170-1175.
Gurney, K., (1997), An Introduction to Neural Networks, UCL Press, London.
Han, J. and Y. Fu, (1999), “Mining Multiple-Level Association Rules in Large Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 5, pp. 789-805.
Han, J. and M. Kamber, (2001), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco.
Heckerman, D., H. Mannila, D. Pregibon and R. Uthurusamy, (1997), Proceedings of The Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA. Cited from T. L. Tseng, M. C. Jothishankar and T. Wu, (2002), “Quality Control Problem in Printed Circuit Board Manufacturing – A Rough Set Based Approach”.
Hsu, Y. Y., F. C. Lu, Y. Chien, J. P. Liu, J. T. Lin, H. S. Yu and R. T. Kuo, (1991), “An Expert System for Locating Distribution System Faults,” IEEE Transactions on Power Delivery, Vol. 6, No. 1, pp. 366-372.
Johns, A. T., L. L. Lai, M. El-Hami and D. J. Daruvala, (1991), “New Approach to Directional Fault Location for Overhead Power Distribution Feeders,” IEE Proceedings C- Generation, Transmission and Distribution, Vol. 138, No. 4, pp. 351-357.
Khoo, L. P. and L. Y. Zhai, (2001), “A Prototype Genetic-Algorithm-Enhanced Rough Set-Based Rule Induction System,” Computers in Industry, Vol. 46, pp. 95-106.
Kleissner, C., (1998), “Data Mining for The Enterprise,” IEEE Proceedings of The Thirty-First Hawaii International Conference on System Sciences, Vol. 7, pp. 295-304.
Kotler, P., (1984), Marketing Management: Analysis Planning and Control, Prentice-Hall, London.
Kusiak, A., (2000), “Decomposition in Data Mining: An Industrial Case Study,” IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 4, pp. 345-353.
Kusiak, A., J. A. Kern, K. H. Kernstine and T. L. Tseng, (2000), “Autonomous Decision-Making: A Data Mining Approach,” IEEE Transactions on Information Technology in Biomedicine, Vol. 4, No. 4, pp. 274-284.
Kusiak, A., (2001), “Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing,” IEEE Transactions on Electronics Packaging Manufacturing, Vol. 24, No. 1, pp. 44-50.
Lee, S. and G. Vachtsevanos, (2002), “An Application of Rough Set Theory to Defect Detection of Automotive Glass,” Mathematics and Computers in Simulation, Vol. 60, pp. 225-231.
Li, B. and K. K. Cho, (2002), “An Inventory Classification Method Using Rough Set Theory,” Proceedings of 7th Annual International Conference, pp. 310-313.
Lo, F. Y., C. F. Chien and J. T. Lin, (2000), “A DEA Study to Evaluate The Relative Efficiency and Investigate The District Reorganization of The Taiwan Power Company,’’ IEEE Transactions on Power System, Vol. 16, No. 1, pp. 170-178.
Pawlak, Z., (1982), “Rough Sets,” International Journal of Computer and Information Sciences, Vol. 11, No. 5, pp. 341-356.
Pawlak, Z., (1991), Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Boston.
Pawlak, Z., (1996), “Why Rough Sets?” Proceedings of The Fifth IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 738-743.
Pawlak, Z., (1997), “Rough Set Approach to Knowledge-Based Decision Support,” European Journal of Operational Research, Vol. 99, pp. 48-57.
Pyle, D., (1999), Data Preparation for Data Mining, Morgan Kaufmann Publishers, San Francisco.
Quinlan, J. R., (1986), “Induction of Decision Trees,” Machine Learning, Vol. 1, Issue 1., pp. 81-106.
Rodrigo, P. D., A. Pahwa and J. E. Boyer, (1996), “Location of Outages in Distribution Systems Based on Statistical Hypotheses Testing,” IEEE Transactions on Power Delivery, Vol. 11, No. 1, pp. 546-551.
Ruhe, G., (1996), “Qualitative Analysis of Software Engineering Data Using Rough Sets,” Proceedings of The Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery, Tokyo, Japan, pp. 292-299.
Shen, L., F. E. H. Tay, L. Qu and Y. Shen, (2000), “Fault Diagnosis Using Rough Sets Theory,” Computers in Industry, Vol. 43, Issue 1, pp. 61-72.
Slowinski, R., C. Zopounidis and A. I. Dimitras, (1997), “Prediction of Company Acquisition in Greece by Means of The Rough Set Approach,” European Journal of Operational Research, Vol. 110, Issue 1, pp. 1-15.
Sovarong, L. and J. S. Costas, (1997), “A General Equipment Diagnostic System and Its Application on Photolithographic Sequences,” IEEE Transactions on Semiconductor Manufacturing, Vol. 10, No. 3, pp. 329-343.
Srikant, R. and R. Agrawal, (1996), “Mining Quantitative Association Rules in Large Relational Tables,” Proceedings of The 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada.
Srikant, R. and R. Agrawal, (1997), “Mining Generalized Association Rules,” Future Generation Computer Systems, Vol. 13, pp. 161-180.
Szolovits, P. and S. Pauker, (1978), “Categorical and Probabilistic Reasoning in Medical Diagnosis,” Artificial Intelligence, Vol. 1, pp. 115-144.
Tay, F. E. H. and L. Shen, (2002), “Economic and Financial Prediction Using Rough Sets Model,” European Journal of Operational Research, Vol. 141, pp. 641-659.
Tomsovic, K., C. C. Liu, P. Ackerman and S. Pope, (1987), “An Expert System As A Dispatchers’ Aid for The Isolation of Line Section Faults,” IEEE Transactions on Power Delivery, Vol. 2, No. 3, pp. 736-743.
Tung, K. H., H. Lu, J. Han and L. Feng, (2003), “Efficient Mining of Intertransaction Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 1, pp. 43-56.
Walczak, B. and D. L. Massart, (1999), “Rough Sets Theory,” Chemometrics and Intelligent Laboratory Systems, Vol. 47, pp. 1-16.
Witten, I. H. and E. Frank, (2000), Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, San Francisco.
Zormana, M., G. Masudab, P. Kokola, R. Yamamotob and B. Stiglica, (2002), “Mining Diabetes Database with Decision Trees and Association Rules,” Proceedings of The 15th IEEE Symposium on Computer-Based Medical Systems, pp. 134-139.