臺灣博碩士論文加值系統

本文旨在應用可拓理論於電力系統的研究，主要研究項目包括電力負載量預測和局部放電(Partial Discharge, PD)之圖譜辨識兩個部份。其中在電力負載預測部分，本文採用一套以物元模型和可拓關聯函數為基礎的可拓預測法，來預測電力系統長期與短期之負載量之變化範圍，再依據各負載類型資料，利用複線性回歸法建立各類型之預測模型，進而計算預測點之長期與短期負載量。最後，在負載預測部分以台灣電力公司實際之運轉實測資料為研究對象。在局部放電之圖譜辨識部份，則是根據商用之局部放電檢測儀來量測高壓比流器的PD圖譜，利用本文提出之數值處理方法萃取其三維空間圖譜之特徵值，再利用各類型故障之特徵值建立PD 圖譜辨識之物元模型，最後以物元模型之間的關聯度來辨識待測高壓比流器的故障類型。在局部圖譜辨識部份，本文以150 筆台灣大電力研究試驗中心的PD 實測資料為研究對象。研究結果顯示，本文提出之方法無論在電力負載預測部分或是部份放電圖譜辨識部份，其皆具有計算速度快、辨識率高與預測準確等特質。

The goal of this thesis is to study the applications of extension theory to load forecasting and partial discharge (PD) pattern recognition in power systems. In the load forecasting, a novel extension clustering method based on the extension theory combined with multi-regression analysis method (MRAM) are introduced to build the load forecasting models for long-term and short-term loads forecasting. Based on the extension theory, the matter-element model can build the every load type model, and then the changed range of long-term and short-term loads at forecasting time can be forecasted according to correlation degree between the built models and the forecasting models. Second, according to the load data of every load type,using the multi-regression analysis method (MRAM) to build the load forecasting models of every load type, then the forecasting models can be used to forecast the values of long-term and short-term loads at forecasting time. To verify the proposed forecasting methods, the statistics data of the real operation in Taiwan have been tested and the methods have given rather encouraging results.
In the PD recognition, this thesis proposes a novel extension based clustering method to recognize the three dimensional (3D) PD patterns of the high voltage cast-resin current transformers (CRCT). First, three data preprocessing schemes that extract relevant features from the raw 3D-PD patterns are presented for the proposed PD recognition method. Second, the matter-element models of the PD defect types are built according to PD patterns derived from practical experimental results, then, the PD defect in a tested CRCT can be directly identified by degrees of correlation between the tested pattern and the matter-element models which have been built up.
To verify the proposed PD recognition method, 150 sets of field-test PD patterns are tested with rather successful results.
The study results verify that the proposed method is computing fast,high recognize and predicting accuracy in electricity load forecasting or the PD recognition.

中文摘要 --------------------------------- i
英文摘要 --------------------------------- ii
致謝 ------------------------------------ iii
目錄 ------------------------------------- iv
表目錄 ----------------------------------- vii
圖目錄 ----------------------------------- ix
符號說明 --------------------------------- xii
第一章 緒論-------------------------------- 1
第二章 可拓理論簡介------------------------- 9
2.1 前言---------------------------------- 9
2.2 物元的概念----------------------------- 10
2.2.1 物元的概念--------------------------- 11
2.2.2 物元的變換--------------------------- 17
2.3 可拓集合理論--------------------------- 19
2.3.1 可拓集合的概念----------------------- 19
2.3.2 關聯函數----------------------------- 21
2.4 本章結論------------------------------- 23
第三章 複線性回歸方法簡介-------------------- 24
3.1 前言---------------------------------- 24
3.2 回歸模型------------------------------- 24
3.2.1 線性回歸分析模型---------------------- 24
3.2.2 最小平方法--------------------------- 26
3.3 複線性迴歸分析模型---------------------- 28
3.4 本章結--------------------------------- 30
第四章 結合可拓理論和複線性回歸法作電力負載預測--------- 31
4.1 前言----------------------------------- 31
4.2 本文所提之預測方法----------------------- 32
4.2.1 長期負載之可拓預測方法------------------ 35
4.2.2 短期負載之可拓預測方法------------------ 37
4.3 實驗結果與討論--------------------------- 40
4.3.1 長期負載預測結果----------------------- 40
4.3.2 短期負載預測結果----------------------- 55
4.4 本章結論-------------------------------- 73
第五章 應用可拓理論於局部放電圖譜辨識----------- 74
5.1 前言------------------------------------ 74
5.2 局部放電檢測系統之架構-------------------- 75
5.3 本文所提的方法--------------------------- 76
5.3.1 可拓圖譜辨識系統之設計------------------ 81
5.4 實驗分析與討論--------------------------- 85
5.4.1 據前置處理結果------------------------- 85
5.4.2 自動化部份放電圖譜辨識系統之實現--------- 88
5.4.3 可拓圖譜辨識系統辨識結果---------------- 91
5.5 本章結論-------------------------------- 93
第六章 結論及未來發展------------------------- 94
6.1 結論------------------------------------ 94
6.2 未來發展--------------------------------- 95
參考文獻 ------------------------------------ 96
附錄A 各預測項目之複線性迴歸方程式-------------- 102

[1]成永紅，電力設備絕緣檢測與診斷，中國電力出版社，第1-5 頁，2001。
[2]Sake, “High-Voltage Test Techniques-Partial Discharge
Measurements ,” IEC 60270, 2001.
[3]楊正光、林育勳、吳瑞南、張宏展，“高壓變壓器局部放電現場查驗方式之研究”，經濟部能源發展基金計劃之期末報告，2002。
[4]許哲強，“台灣區域電力負載預測分析系統之建立與應用研究”，國立成功大學，博士論文，2002。
[5]D. W. Ross, et al., “Short-Term Load Prediction for Economic Dispatch of Generation,” IEEE Proc. PICA, Vol. 79, pp.198-204, 1979.
[6]A. Keyhani, T. E. Rad, “A Simulation Study for Recursive
Prediction of Hourly Demands,” IEEE Proc. PICA, pp. 228-236,
[7]N. Amjady, “ Short-Term Hourly Load Forecasting Using
Time-Series Modeling with Peak Load EstimationCapability,” IEEE Trans. on Power Systems , Vol. 16, No. 4, pp.798-805, 2001.
[8]F. J. Nogales, J. Contreras, A.J. Conejo, and R. Espinola,“ Forecasting Next-Day Electricity Prices by Time Series Models,”IEEE Trans. on Power Systems, Vol. 17,No.2, pp. 342-348, 2002.
[9]I. Moghram, S. Rahman, “Analysis and Evaluation of Five
Short-Term Load Forecasting Techniques,” IEEE Trans. on Power Systems, Vol.4, No. 4, pp.1484-1491, 1989.
[10]W. R. Christiaanse, “Short-Term Load Forecasting Using General Exponential Smoothing,” IEEE Trans. on Power Systems, Vol.PAS-90, No.2, pp.900 - 911, 1971.
[11]G. Gross, F. D. Galiana, “Short-Term Load Forecasting,”Proceedings of the IEEE, Vol. 75, No.12, pp.1558 - 1573,December, 1987.
[12]H. P. Van Meeteren, P. J. M Van Son, “Short-Term Load Prediction with a Combination of Different Models,” IEEE Proc. PICA, Vol. 79,pp. 190-197, 1979.
[13]R. P. Thompson, “Weather Sensitive Demand and Energy Analysis on a Large Geographically Diverse Power System — Application to Short-Term Hourly Electric Demand Forecasting,” IEEE Trans. PAS,Vol. 95, pp. 384-393, 1976.
[14]P. C. Gupta, K. Yamada, “Adaptive Short-Term Forecasting of Hourly Loads Using Weather Information,” , presented at the IEEE/PES Winter Meeting, Paper 72 WM T72-170-4 PES, 1972.
[15]M. T. Hagan, S. M. Behr, “The Time Series Approach to Short Term Load Forecasting,” IEEE Trans. on Power Systems, Vol. 2, No.3, pp.785-791, 1987.
[16]T. Haida, S. Muto, “Regression Based Peak Load Forecasting Using a Transformation Technique,” IEEE Trans. on Power Systems, Vol. 9,No. 4, pp. 1788-1794, November, 1994.
[17]Hagan, Demuth, Beale, “Neural Network Design,” Thomson
learning，1996。
[18]葉怡成，類神經網路模式應用與實作，儒林圖書，2003。
[19]T. Onoda, “Next Day Peak Load Forecasting Using an Artificial Neural Network with Modified Backpropagation Learning Algorithm,” IEEE Proc. Neural Networks, Vol. 6, pp. 3766-3769,1994.
[20]T. Onoda, “Next Day's Peak Load Forecasting Using an Artificial Neural Network,” IEEE Proc. ANNPS, pp. 284 –289, 1993.
[21]S. S. Sharif, J. H. Taylor, “Real-Time Load Forecasting by Artificial Neural Networks,” IEEE Power Engineering Society Summer Meeting, Vol. 1, pp. 496–501, 2000.
[22]J. K. Mandal, A. K. Sinha, “Artificial Neural Network Based Hourly Load Forecasting for Decentralized Load Management,” IEEE Proc.EMPD, Vol. 1, pp. 61–66, 1995.
[23]C. N. Lu, H. T. Wu, and S. Vemuri, “Neural Network Based Short Term Load Forecasting,” IEEE Trans. on Power Systems, Vol. 8,No.1, pp.336-342, 1993.
[24]D. C. Park, M. A. El-Sharkawi, R. J. Marks, L. E. Atlas, and M. J.Damborg, “Electric Load Forecasting Using an Artificial Neural Network,” IEEE Trans. on Power Systems, Vol.6, No.2, pp. 442-449,1991.
[25]K. T. Lee, Y. T. Cha, and J. H. Park, “Short-term Load Forecasting Using an Artificial Neural Network,” IEEE PES Winter Power Meeting, Paper No. 91 WM199-OPWRS, 1991.
[26]A. G. Bakirtzis, J. B. Theocharis, S. J. Kiartzis and K. J. Satsios,“ Short Term Load Forecasting Using Fuzzy Neural Network,”IEEE Trans. on Power Systems, Vol. 10, pp. 1518-1524, 1995.
[27]S. J. Kiartzis, A. G. Bakirtzis, J. B. Theocharis and Gr. Tsagas, “A Fuzzy Expert System for Peak Load Forecasting. Application to the Greek Power System,” IEEE Proc. MELECON, Vol. 3, pp.1097-1100, 2000.
[28]H. Mori, Y. Sone, “A Simplified Fuzzy Inference Method with Tabu Search for Short-Term Load Forecasting in Power Systems,” IEEE Proc. Decision and Control, Vol. 4, pp. 4240-4245, 1998.
[29]H. Mori, Y. Sone, D. Moridera and T. Kondo, “Fuzzy Inference Models for Short-Term Load Forecasting with Tabu Search,” IEEE Proc. SMC, Vol. 6, pp. 551-556, 1999.
[30]H. Mori, H. Kobayashi, “Optimal Fuzzy Inference for Short-Term Load Forecasting,” IEEE Trans. on Power Systems, Vol. 11, No. 1,pp. 390-396, 1996.
[31]Mo-yuen Chow, Hahn Tram, “Application of Fuzzy Logic
Technology for Spatial Load Forecasting,” IEEE Proc. Transmission and Distribution, pp. 608-614, 1996.
[32]Y. Y. Hsu, K. L. Ho, “Fuzzy Expert Systems: An Application to Short-Term Load Forecasting,” IEE Proc. Generation, Transmission and Distribution, Vol. 139, No. 6, pp. 471-477, 1992.
[33]G. Lambert-Torres, C. O. Traore, F. G. Mandolesi and D.
Mukhedkar, “Short-Term Load Forecasting Using a Fuzzy
Engineering Tool,” IEEE Proc. ANNPS, pp. 36-40, 1991.
[34]S. Ranman, R. Bhatnager, “An Expert System Based Algorithm for Short-Term Load Forecasting,” IEEE Trans. on Power Systems, Vol.3, No.2, pp. 392 - 399, May, 1988.
[35]H. T. Yang, T. C. Liang, K. R. Shih and C. L. Huang, “Power System Yearly Peak Load Forecasting: A Grey System Modeling Approach,” IEEE Proc. EMPD, Vol. 1, pp. 261 –266, 1995.
[36]鄧聚龍，灰色系統理論與應用，高立圖書，2003。
[37]E. Gulski, “Digital Analysis of Partial Discharges,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 2, No. 5,pp. 822-837, Oct. 1995.
[38]F. H. Kreuger, E. Gulski, and A. Krivda, “Classificaction of Partial Discharge,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 28, No. 6, pp. 917-931,1993.
[39]A. Krivda, “Automated Recognition of Partial Discharge,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 2, No. 5,pp. 796-821, Oct. 1995.
[40]H. G. Kranz, “Diagnosis of Partial Discharge Signals Using Neural Networks and Minimum Distance Classification,” IEEE Transactions on Electrical Insulation, Vol. 26, No. 6, pp. 1016-1024,1993.
[41]J. S. Foo, P. S. Ghosh, “Artificial Neural Network Modeling of Partial Discharge Parameters for Transformer Oil Diagnosis,” 2002 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, pp. 470-475, 2002.
[42]E. Gulski, A. Krivda, “ Neural Networks as a Tool for Recognition of Partial Discharges,” IEEE Transactions on Electrical Insulation,Vol. 27, pp. 550-556, 1992.
[43]J. W. Shiu, “Application of Artificial Neural Network to the Partial Discharge Recognition of Current Transformers,” Taiwan University of Science and Technology, Master’s Thesis, 2003.
[44]K. Tomsovic, M. Tapper, and T. T. Ingvarsson, “A Fuzzy Information Approach to Integrating Different Transformer Diagnostic Methods,”IEEE Transactions on Power Delivery,Vol.8, No.3, pp.1638-1643,July, 1993.
[45]H. G. Kranz, R. Krump, “Partial Discharge Diagnosis UsingStatistical Parameter Optimization on a PC based System,” IEEE Transactions on Electrical Insulation, Vol. 27, No. 1, pp. 93-98,1992.
[46]H. S. Hippert, Carlos Eduardo Pedreira, and Reinaldo Castro Souza,“Neural Networks for Short-Term Load Forecasting: A Review and Evaluation,” IEEE Trans. Power Systems, Vol. 16, No. 1, pp. 44-55,2001.
[47]W. Cai, “The Extension Set and Incompatibility Problem,” J. of Scientific Exploration, Vol. 1, pp.81-93, 1983.
[48] W. Cai, “Extension Set, Fuzzy Set and Classical Set,” First Congress of International Fuzzy System Association, Spain, 1985.
[49]蔡文、楊春燕、林傳初原著，黃有評、邱紹維、游力川編譯，可拓工程方法，全華科技圖書有限公司，2001。
[50]Y. P. Huang, H. J. Chen, “The Extension-Based Fuzzy Modeling Method and Its Applications,” in Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, Edmonton, Alberta, Canada,pp. 977-982, 1999.
[51]M. H. Wang, H. C. Chen, “Application of Extension Theory to the Fault Diagnosis of Power Transformers,” Proceeding of the 22nd Symposium on Electrical Power Engineering, pp.797-800, 2001.
[52]John Neter William Wasserman 原著，吳宗政編譯，迴歸分析理論與應用，復文書局，1980。
[53]李忠恩，“利用迴歸法之短期負載預測”，台灣大學，碩士論文，1989。
[54]徐克學，“生物數學”，科學出版社，pp.23-40，1999。
[55]行政院主主計處第三局統計資料心，http://www.dgbasey.gov.tw/。
[56]台灣電力公司， http : //www taipower com tw/ home .php. 。
[57]J. L. Tong, “ The Grey System Theory and Applications,” The Science Press, China, 1991.
[58]大氣研究資料庫， http://stdank.as.ntu.edu.tw/。
[59]許晉維，“應用類神經網路於比流氣局部放電圖譜之辨識”，國立台灣科技大學電機所，碩士論文，2003。
[60]R. O. Dude, P. E. Hart, Pattern Classification and Scene Analysis,Wiley, New York, 1973.