可靠度成本分析在配電系統規劃及運轉中是相當重要的問題，而斷電成本模型則直接影響可靠度成本分析之準確性。本論文配合平均斷電成本模型及機率分佈斷電成本模型分兩階段進行可靠度成本分析。第一階段本論文提出以平均斷電成本模型為基礎，對系統可靠度成本進行分析推導，得到可靠度成本模型可以表示為各饋線段電力流量的線性函數，並配合各項投資固定成本、各饋線電阻損失能量成本以及運轉限制式，完成配電規劃數學模式，並利用進化規劃法執行配電系統網路架構最佳規劃的問題，並以一台電配電系統為例子，建立配電規劃的數學模式，並藉由進化規劃法的尋優進化過程中，尋求整體配電系統網路架構的最佳解。第二階段本論文提出以利用放射狀基礎函數類神經網路整合不同類型用戶之機率分佈成本模型，同時配合蒙地卡羅時序模擬方法進行配電系統可靠度成本之分析，並以一台電配電系統為例進行可靠度成本分析，由結果顯示，兩種斷電成本模型所得到的可靠度成本差異甚大，而採用機率分佈斷電成本模型可得到系統更實際之可靠度成本。

Reliability worth analysis is an important tool for distribution systems planning and operations. The interruption cost model used in the analysis directly affects the accuracy of the reliability worth evaluation. In this dissertation, the reliability worth analysis was dealt with two interruption cost models including an average or aggregated model (AAM), and a probabilistic distribution model (PDM) in two phases. In the first phase, the dissertation presents a reliability cost model based AAM for distribution system planning. The reliability cost model has been derived as a linear function of line flows for evaluating the outages. The objective is to minimize the total cost including the outage cost, feeder resistive loss, and fixed investment cost. The Evolutionary Programming (EP) was used to solve the very complicated mixed-integer, highly non-linear, and non-differential problem. A real distribution network was modeled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process. In the second phase, the interruption cost model PDM was proposed by using the radial basis function (RBF) neural network with orthogonal least-squares (OLS) learning method. The residential and industrial interruption costs in PDM were integrated by the proposed neural network technique. A Monte-Carlo time sequential simulation technique was adopted for worth assessment. The technique is tested by evaluating the reliability worth of a Taipower system for the installation of disconnected switches, lateral fuses, transformers and alternative supplies. The results show that the two cost models result in very different interruption costs, and PDM may be more realistic in modeling the system.

目 錄

誌謝……………………………………………. I
中文摘要………………………………………. III
英文摘要………………………………………. IV
目錄……………………………………………. V
圖目錄…………………………………………. IX
表目錄…………………………………………. XI

第一章 緒論…………………………………. 1
1-1 研究背景及方法…………………………………. 1
1-2 主要貢獻………………….…………………….… 9
1-3 論文內容概述………………………………..…... 11
第二章 配電系統可靠度計算及評估………. 14
2.1前言………………………………………………… 14
2.2可靠度成本指標………………………………….. 15
2.2.1一般化配電系統架構………..………………… 16
2.2.2網路簡化……………………………………….. 19
2.2.3用戶導向指標…………………………………... 21
2.2.4負載及能量導向指標…………………………... 21
2. 2. 5實例說明………………………………………. 22
2.3本章結論…………………………………………… 31
第三章 考慮可靠度成本之配電系統規劃…. 32
3.1前言…………………………………………………. 32
3.2可靠度模型推導………………………………….. 32
3.2.1用戶斷電成本計算…….……………………….. 34
3.2.2系統規劃目標函數…………………….……….. 38
3.3進化規劃法應用………………………...………… 39
3.3.1進化規劃法介紹………………………………... 40
3.3.2配電系統規劃應用…………….……………….. 45
3.4成本估算方法………………………………… 48
3.5本章結論…………………………………………… 51
第四章 機率分佈成本模型可靠度指標……. 52
4.1前言…………………………………………………. 52
4.2放射狀基礎函數類神經網路…………………… 53
4.3正交化最小平方學習程序……………………… 55
4.4機率分佈成本模型理論……………………… 58
4.5整合配電機率分佈成本模型之建立………… 62
4.6本章結論…………………………………………… 63
第五章 蒙地卡羅時序模擬法可靠度評估…. 64
5.1 前言………………………………………………… 64
5.2元件模型及參數………………………………….. 65
5.3負載點及系統之可靠度指標及其分佈……… 67
5.4成本取樣程序………………………………… 69
5.5蒙地卡羅時序模擬法………………………… 70
5.6 本章結論………………………………………….. 74
第六章 系統可靠度評估與整合測試………. 75
6.1前言…………………………………………… 75
6.2配電系統規劃測試…………………………… 76
6.2.1系統資料……….……………………………….. 76
6.2.2配電規劃測試結果……………………………... 80
6.3放射狀基礎函數類神經網路效能測試……… 85
6.3.1收歛性測試……………………………………... 85
6.3.2效能測試………………………………………... 85
6.4 配電系統可靠度評估測試………………….. 87
6.4.1系統資料……….……………………………….. 87
6.4.2用戶成本模型資料………………..……………. 90
6.4.3可靠度評估模擬結果…………………………... 90
6.5本章結論……………………………………… 96
第七章 結論及未來發展方向………………. 97
7.1結論…………………………………………………. 97
7.2未來發展方向…………………………………….. 100
參考文獻……………………………….……… 103
著作目錄……………………………….……… 109
作者簡歷……………………………….……… 111


圖目錄


圖1-1: 電力系統可靠度評估階層………………………………... 2
圖1-2: 典型電力系統事故的平均無效度………………………... 3
圖1-3: 配電系統可靠度與總成本之關係曲線.………………….. 7
圖2-1: 一般化幅射狀架構配電系統……………………………... 17
圖2-2: 網路簡化…………………………………………………... 20
圖2-3: 簡單的配電系統…………………………………………... 24
圖2-4: 複雜的配電系統…………………………………………... 27
圖3-1: 典型的輻射狀配電系統架構……………………………... 34
圖3-2: 各類型用戶斷電損失函數………………………………... 37
圖3-3: 進化規劃演算法之流程圖………………………………... 44
圖4-1: 放射狀基礎函數類神經網路之架構………….………….. 54
圖4-2: 放射狀基礎函數類神經網路為基礎的機率分佈成本模型架構……………………………………………………. 63
圖5-1： 輸電線與變壓器之二狀態模型(Two-state model)………. 66
圖5-2： 配電系統元件的運轉-修復歷史………………………… 66
圖5-3： 負載點的運轉-修復歷史………………………………… 67
圖5-4： 成本取樣程序之流程圖…………………………………. 71
圖5-5： 蒙地卡羅時序模擬法進行配電系統可靠度評估之流程. 73
圖6-1: 測試之台電配電系統……………………………………... 77
圖6-2: 不考慮可靠度成本時EP的收斂特性…….……………… 81
圖6-3: 不考慮可靠度成本的配電系統最佳化規劃架構………... 81
圖6-4: 考慮可靠度成本時EP的收斂特性………………………. 84
圖6-5: 考慮可靠度成本之配電系統最佳化規劃架構…………. 84
圖6-6: BP及RBF類神經網路訓練過程之收歛特性圖……….. 86
圖6-7: 以RBFNN建立工業型用戶的機率分佈成本模型參數… 87
圖6-8: 進行可靠度評估測試之台電配電系統………………….. 88
圖6-9: 負載點1之TTF、TTR機率分佈長條圖…………………. 94
圖6-10: 負載點22之TTF、TTR機率分佈長條圖……………….. 94
圖6-11: 負載點1之斷電成本機率分佈長條圖…………………… 95
圖6-12: 負載點22之斷電成本機率分佈長條圖………………….. 95

表目錄

表2-1: 各饋線段之年平均故障率……………………………….. 24
表2-2: 各負載點之平均負載及用電戶數……………………….. 24
表2-3: 負載點指標……………………………………………….. 25
表2-4: 各饋線段之年平均故障率………………………………... 28
表2-5: 各負載點之平均負載及用電戶數………………………... 29
表2-6: 負載點指標………………………………………………... 30
表2-7: 系統可靠度指標(單位與案例1相同)…………………… 30
表4-1: 住宅型用戶機率分佈成本模型之參數…………………... 60
表4-2: 工業型用戶機率分佈成本模型之參數…………………... 60
表6-1: 一般投資資料……………………………………………... 78
表6-2: 負載點用戶資料…………………………………………. 79
表6-3: 主要設備之故障率及故障期間…………………………. 79
表6-4: 不考慮可靠度成本下配電系統最佳化之全部成本結果. 83
表6-5: 考慮可靠度成本下配電系統最佳化之全部成本結果…. 83
表6-6: BP及RBF類神經網路不同誤差標準時之收歛率……… 86
表6-7: 負載點用戶資料………………………………………….. 89
表6-8: 主要設備之平均故障率及故障修復期間……………….. 89
表6-9: 住宅及工業類型用戶之斷電損失函數…………………... 90
表6-10: 負載點之可靠度指標….………………………………….. 91
表6-11: 系統之可靠度指標………………………………………... 92

參考文獻

[1] L. Goel and R. Billinton, “Determination of Reliability Worth for Distribution System Planning,” IEEE Trans. on Power Delivery, Vol. 9, No.3, July 1994, pp.1577-1583.
[2] L. Goel and R. Billinton, “A Procedure for Evaluating Interrpted Energy Assessment Rates in an Overall Electric Power System,” IEEE Trans. on Power Systems, Vol. 6, No.4, Nov. 1991, pp.1396-1403.
[3] J. Oteng-Adjei and R. Billinton, “Evaluation of Interrupted Energy Assessment Rates in Composite Systems,” IEEE Trans. on Power Systems, Vol. 5, No.4, Nov. 1990, pp.1317-1323.
[4] R.N. Allan and R. Billinton, “Power system reliability and its assessment: Part 1 Background and generating capacity,” Power Engineering Journal, Vol. 6, No. 4, 1992, pp. 191-196.
[5] R.N. Allan and R. Billinton, “Power system reliability and its assessment: Part 2 Composite generation and transmission system,” Power Engineering Journal, Vol. 6, No. 6, 1992, pp. 291-297.
[6] R.N. Allan and R. Billinton, “Power system reliability and its assessment: Part 3 Distribution systems and economic considerations,” Power Engineering Journal, Vol. 6, No. 6, Aug. 1993, pp. 185-192.
[7] R. Billinton and R.N. Allan, “Reliability evaluation of power systems,” Plenum Publishing, New York, 1984.
[8] R. Billinton, and S. Jonnavithula, “A test system for teaching overall power system reliability assessment,” IEEE Trans. on Power Systems, Vol. 11, No.1, 1996, pp.1670-1676.
[9] R. Billinton, and P. Wang, “Reliability-network-equivalent approach to distribution-system-reliability evaluation, ” IEE Proc.-Gener. Trans. Distrib., Vol. 145, No. 2, 1998, pp.149-153.
[10] R.N. Allan, and, M.G. DA SILVA, “Evaluation of reliability indices and outage costs in distribution systems, ” IEEE Trans. on Power Systems, Vol.10, No.1, 1995, pp. 413-419.
[11] R.E. Brown, S. Gupta, R.D. Christie, S.S. Venkata, and R. Fletcher, “Automated primary distribution system design: reliability and cost optimization,” IEEE Trans. on Power Delivery, Vol. 12, No. 2, 1997, pp. 1017-1022.
[12] V.H. Quintana, H.K. Temraz, and K.W. Heipel, “Two-stage power system distribution planning algorithm, ” IEE Proceedings-C, Vol. 140, Jan. 1993, pp. 17-29.
[13] R. Billinton and P. Wang, “Teaching distribution system reliability evaluation using Monte Carlo simulation, ” IEEE Trans. on Power System, Vol. 14, No. 2 , 1999, pp. 397-403.
[14] R.L. Chen, K. Allen, and R. Billinton, “Value-based distribution reliability assessment and planning, ” IEEE Trans. on Power Delivery, Vol. 10, No. 1 , 1995, pp. 421-429.
[15] G. Wacker and R. Billinton, “Customer cost of electric service interruptions,” Proceedings of IEEE, Vol. 77, No. 6, June 1989, pp. 919-930.
[16] G. Tollefson, R. Billinton and G. Wacker, “Comprehensive bibliography on reliability worth and electrical service consumer interruption costs,” IEEE Trans. on Power Systems, Vol. 6, No. 4, Nov. 1991, pp. 1508-1514.
[17] G. Tollefson, R. Billinton, G. Wacker, E. Chan and J. Aweya, “Canadian customer survey to assess power system reliability worth,” IEEE Trans. on Power Systems, Vol. 9, No. 1, Nov. 1994, pp. 1508-1514.
[18] R. Ghajar, R. Billinton, and E. Chan, “Distributed nature of residential customer outage costs, ” IEEE Trans. on Power System, Vol. 11, No. 3 , 1996, pp. 1236-1244.
[19] R. Billinton, E. Chan, and G. Wacker, “Probability distribution approach to describe customer costs due to electric supply interruptions,” IEE Proc.-Gener., Trans., Distrib., Vol. 141, No. 6, Nov. 1994, pp.594-598.
[20] R. Billinton, and P. Wang, “Reliability worth of distribution system network reinforcement considering dispersed customer cost data, ” IEE Proc. Gener. Trans,. Distrib., Vol. 146, No. 3, 1999, pp. 318-324.
[21] D.V. Coury and D.C. Jorge: “Artificial neural network approach to distance protection of transmission lines’, IEEE Trans. On Power Delivery, Vol. 13, No. 1, 1998, pp. 102-108.
[22] H. Wang, and W.W. Keerthipala, “Fuzzy-neuro approach to fault classification for transmission line protection, ” IEEE Trans. On Power Delivery, Vol. 13, No. 4 , 1998, pp. 1093-1102.
[23] W.M. Lin, C.D. Yang, J.H. Lin and M.T. Tsay, “A fault classification method by RBF neural network with OLS learning procedure, ” IEEE Trans. On Power Delivery, Vol. 16, No. 4 , 2001, pp. 473-477.
[24] R.K. Aggarwal, Q.Y. Xuan, R.W. Dunn, A.T. Johns, and A. Bennett, “A novel fault classification technique for double-circuit lines based on a combined unsupervised /supervised neural network,” IEEE Trans. on Power Delivery, Vol. 14, No. 4 , 1999, pp. 1250-1255.
[25] S. Chen, C.F.N. Cowan and P.M. Grant, “Orthogonal least squares learning algorithm for radial basis function networks, ” IEEE Trans. on Neural Network, Vol. 2, No. 2 , 1991, pp. 302-309.
[26] S. Chen, C.F.N. Cowan, and P.M. Grant, “Orthogonal least squares learning algorithm for training multioutput radial basis function networks,” IEE PROCEEDINGS-F, Vol. 139, No. 6, 1992, pp. 378-384.
[27] R. Billinton and J. E. Billinton, “Distribution system reliability indices,” IEEE Trans. on Power Delivery, Vol. 4, No. 1, Jan. 1989, pp. 561-568.
[28] L. Goel and R. Billinton, “Evaluation of interrupted energy assessment rates in distribution systems,” IEEE Trans. on Power Delivery, Vol. 6, No. 4, Oct. 1991, pp. 1876-1882.。
[29] G. Toefson, R. Billinton and G. Wacker, “Comprehensive bibliography on reliability worth and electric service consumer interruption costs,” IEEE Trans. on Power Systems, Vol.6, No.4, 1991, pp. 1508-1514.
[30] R. Billinton and J. Oteng-Adjei, “Utilization of interrupted energy assessment rates in generation and transmission system planning,” IEEE Trans. on Power Systems, Vol.6, No.3, Aug. 1991, pp. 1245-1253.
[31] J.C.O. Mello, M.V.F. Pereira and A.M. Leite da Silva, “Evaluation of reliability worth in composite systems based on pseudo-sequential Monte Carlo simulation,” IEEE Trans. on Power Systems, Vol. 11, No. 3, Aug. 1996, pp. 1255-1260.
[32] R. Billinton and P. Wang, “A generalized method for distribution system reliability evaluation,” Proceedings of the 1995 WESCANEX IEEE International Conference, pp.349-354.
[33] A. Sankarakrishnan and R. Billinton, “Effective techniques for reliability worth assessment in composite system networks using Monte Carlo simulation,” IEEE Trans. on Power Systems, Vol. 9, No. 3, Aug. 1994, pp. 1318-1326.
[34] Y. Ou and L. Goel, “Using Monte Carlo simulation for overall distribution system reliability worth assessment,” IEE Proc.-Gener. Trans. Distrib., Vol. 146, No. 5, 1999, pp.535-540.
[35] R. Billinton and P. Wang, “Distribution system reliability cost/worth analysis using analytical and sequential simulation techniques,” IEEE Trans. on Power Systems, Vol.13, No.4, Nov. 1998, pp. 1245-1250.
[36] R.N. Allan, R. Billinton, I. Sjarief, L. Goel and K.S. So, “A reliability test system for educational purposes – Basic distribution system data and results,” IEEE Trans. on Power Systems, Vol. 6, No. 2, May 1991, pp. 813-820.
[37] R. Billinton and R.N. Allan, “Reliability evaluation of engineering systems: concepts and techniques,” Plenum Publishing, New York, 1992.
[38] D.M. Crawford and S.B. Holt, “A mathematical optimization technique for locating and sizing distribution substations, and deriving their optimal service areas, ” IEEE Trans. on PAS, Vol. PAS-99, No.2, March/April, 1975, pp.230-235.
[39] D.L. Wall, G.L. Thompson, and J.E.D. Northcote-Green, “An optimal model for planning radial distribution network,” IEEE Trans. on PAS, Vol. PAS-98, No.3, May/June, 1979, pp.1061-1068.
[40] J.T. Boardman and C.C. Meckiff, “A branch and bound formulation to an electricity distribution planning problem,” IEEE Trans on PAS, Vol. PAS-104, No.8, August 1985, pp.2112-2118.
[41] R.N. Adams and M.A. Laughton, “A dynamic programming network flow procedure for distribution systems,” Proc. 8th, IEEE PICA, 1973, pp.348-354.
[42] T. Gonen and I.J. Ramirez-Rosado, “Optimal Multi-Stage Planning of Power Distribution Systems," IEEE Trans on PWRD, Vol. PWRD-2, No.2, April 1987, pp.512-519.
[43] M. Ponnavaikko, K.S. Prakasa, and S.S. Venkata, “Distribution system planning through a quadratic mixed integer programming approach,” IEEE Trans., PWRD-2, No.4, October 1987, pp. 1157-1163.
[44] T.H. Fawzi, K.F. Ali, and S.M. El-Sobki, “Routing optimization of primary rural distribution feeders,” IEEE Trans. on PAS, Vol. PAS-101, No.5, May 1982, pp.1129-1133.
[45] 楊維楨和廖添丁，"配電系統規劃研究(一)，(二)，(三)，"台電工程月刊，1989.
[46] K.S. Hindi and Brameller, “Design of low-voltage distribution networks:a mathematical programming method”,