Responsive image
博碩士論文 etd-0714117-123420 詳細資訊
Title page for etd-0714117-123420
論文名稱
Title
強化配電系統狀態估計準確性之計量電表設置
Meter Placement for Distribution System State Estimation Enhancement
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
123
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2017-07-26
繳交日期
Date of Submission
2017-08-14
關鍵字
Keywords
配電系統可觀測性分析、計量電表設置、圖論、概率配電系統狀態估計
Probabilistic Distribution System State Estimation, Graph Theory, Meter Placement, Distribution Network Observability Analysis
統計
Statistics
本論文已被瀏覽 5658 次,被下載 86
The thesis/dissertation has been browsed 5658 times, has been downloaded 86 times.
中文摘要
可觀測性分析為狀態估計中相當重要的前置作業,需有足夠的量測值才能執行狀態估計。傳統的可觀測性分析,可分為數值方法與拓樸方法。這樣的定義適合在輸電系統中使用,因為輸電系統之中有大量的設備可提供量測值。而配電系統基於經濟考量,無法裝設大量的儀表設施。因此,為了使配電系統能夠執行狀態估計,加入虛擬量測值為配電系統狀態估計常使用的方法。虛擬量測值可利用歷史資料加以估計而取得。如此一來便能使配電系統狀態估計順利執行。然而在大量的虛擬量測值加入後,配電系統狀態雖然可計算,但虛擬量測值的加入會導致計算後的結果與實際系統情況不同。本論文提出適合配電系統狀態估計適合的配電系統可觀測性分析定義,其內容是規範每一個欲估計參數的估計誤差。若系統不可觀測時,則需要增設計量電表以提供精準的量測值。配電系統設置電表的相關文獻中,常使用機率論相關的方法進行研究。而本論文結合圖論與機率論,提出計量電表的放置方法。透過圖論先篩選出系統中較為關鍵的地點,再配合估計誤差的分析,最後決定新增電表的位置。模擬測試部分包含三種系統、七種不同的裝置電表方法,以及假設部分地區裝設先進讀表基礎設施,進行各項比較分析。模擬過程應用概率配電系統狀態估計,降低量測值誤差所造成的影響。本研究使用點估計(Point Estimate Method)進行狀態估計,其計算量遠小於蒙地卡羅法(Monte Carlo Simulation),其效益利用F-檢定加以驗證。本論文在符合可觀測性的情況下,提出最少數量的計量電表及其增設位置。
Abstract
The observability analysis is an essential step in state estimation process. It determines whether the states of a system can be estimated using available measurements. This definition works well in transmission networks, where numerous accurate metering devices are normally available. However, in distribution networks, only a few metering devices are usually installed. Therefore, pseudo measurements with large margins of error are often used in the absence of real measurements to perform Distribution System State Estimation (DSSE). This implies that if many pseudo measurements are used to make a network observable, the estimated states can be significantly different from the actual state even if the network is classified as observable. This thesis proposes a proper observability requirement for DSSE based on estimation accuracy. The concept is to limit estimate error for each estimated parameter. This study incorporates graph theory and probability theory. The proposed meter placement algorithm provides candidate locations for additional meter placements. The candidate locations Simulations are conducted on three networks with seven meter placement methods. A probabilistic load flow technique based on point estimate method is used to validate the accuracy of DSSE results. The proposed method suggests a minimum number of additional meters and their locations to satisfy the observability requirement adopted in this research.
目次 Table of Contents
論文審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
Table of Contents v
List of Figures vii
List of Tables x
Chapter 1 Introduction 1
1.1 Research Background and Objectives 1
1.2 Literature Review 3
1.2.1 Distribution Network Modeling 3
1.2.2 Distribution System State Estimation 11
1.2.3 Observability Analysis and Meter Placement 13
1.2.4 Probabilistic Load Flow (PLF) 16
1.3 Research Contributions 23
1.4 Thesis Structure 23
Chapter 2 Distribution System State Estimation Meter Placement 24
2.1 Measurement and Meter Placement 24
2.2 Polar Form Node Voltage Based Distribution System State Estimation 28
2.3 Graph Theory Application in Meter Placement 32
2.2.1 Articulation Point Method 32
2.2.2 Vertex Cover Method 34
2.2.3 Maximal Independent Set Method 36
2.4 Probabilistic Distribution System State Estimation (PDSSE) 38
2.4.1 Monte Carlo Simulation Based PDSSE 38
2.4.2 Point Estimate Method Based PDSSE 38
2.4.3 F-test for Point Estimate Method Based PDSSE 39
2.5 The Proposed Algorithm 40
2.5.1 Meter Placement Candidate Determination 40
2.5.2 The Proposed Meter Placement Procedure 41
2.5.3 Observability Definition and Requirement 42
Chapter 3 Numerical Results 44
3.1 Test Systems 44
3.2 PDSSE Results 48
3.3 Distribution Meter Placement Results 51
3.3.1 Test Cases 51
3.3.2 Test Results 52
3.4 Discussions 66
Chapter 4 Conclusion and Future Work 67
4.1 Conclusion 67
4.2 Future Work 68
References 69
Appendix A Configurations, Bus Loads and Line Parameters of Test Systems 72
Appendix B Candidate Buses Obtained from Graph Theory for Meter Placement in Different Test Systems 80
Appendix C P-value in F-test of PEM and MCS Result Comparisons 84
Appendix D Best Meter Locations Obtained by Different Methods in Different Test Systems 90
Appendix E Comparison of Different Observability Requirements 109
參考文獻 References
[1] F. Pilo, G. Pisano and G. G. Soma, “Advanced DMS to manage active distribution networks,” in IEEE Power Tech Conference, Bucharest, pp. 1-8, 2009.
[2] G. Celli, P. A. Pegoraro, F. Pilo, G. Pisano, and S. Sulis, “DMS cyber-physical simulation for assessing the impact of state estimation and communication media in smart grid operation,” IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2436-2446, 2014.
[3] “智慧型電網(AMI)之發展應用趨勢篇” FGT-線上技術月刊,有效網址:https://fgttw.com/tw/news_view.php?view=44
[4] “低壓智慧型電表推動規劃”經濟部能源農業處,有效網址:http://www.ey.gov.tw/News_Content.aspx?n=4E506D8D07B5A38D&s=8D350E6A99032971
[5] A. Monticelli, “Electric power system state estimation,” Proceedings of the IEEE, vol. 88 no. 2, pp. 262-282, 2000.
[6] F. F. Wu, “Power system state estimation: a survey,” International Journal of Electrical Power & Energy Systems, vol. 12, no. 2, pp. 80-87, 1990.
[7] D. L. Lubkeman, J. Zhang, A. K. Ghosh, and R. H. Jones, “Field results for a distribution circuit state estimator implementation,” IEEE Transactions on Power Delivery, vol. 15, no. 1, pp. 399-406, 2000.
[8] R. F. Arritt, and R. C. Dugan, “Distribution system analysis and the future smart grid,” In Rural Electric Power Conference (REPC) IEEE pp. B2-1, 2011.
[9] G. W. Chang, S. Y. Chu, and H. L. Wang, “A simplified forward and backward sweep approach for distribution system load flow analysis,” IEEE In Power System Technology International Conference pp. 1-5, 2006.
[10] R. Arritt, and R. Dugan, “Comparing load estimation methods for distribution system analysis,” 2013.
[11] R. Singh, B. C. Pal, and R. A. Jabr, “Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement,” IET generation, transmission & distribution, vol. 4, no. 1, pp. 50-59, 2010.
[12] E. Manitsas, R. Singh, B. C. Pal, and G. Strbac, “Distribution system state estimation using an artificial neural network approach for pseudo measurement modeling,” IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1888-1896, 2012.
[13] M. K. Celik, and W. H. Liu, “A practical distribution state calculation algorithm,” In Power Engineering Society 1999 Winter Meeting, IEEE vol. 1, pp. 442-447, 1999.
[14] C. N. Lu, J. H. Teng, and W. H. Liu, “Distribution system state estimation,” IEEE Transactions on Power Systems vol. 10, no. 1, pp. 229-240, 1995.
[15] W. M. Lin, and J. H. Teng, “State estimation for distribution systems with zero-injection constraints,” IEEE Transactions on Power Systems, vol. 11, no. 1, pp. 518-524, 1996.
[16] A. Abur and A. G. Exposito, Power System State Estimation: Theory and Implementation, Marcel Dekker, Inc, 2004.
[17] B. Brinkmann, and M. Negnevitsky, “A probabilistic approach to observability of distribution networks,” IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 1169-1178, 2017.
[18] N. C. Koutsoukis, N. M. Manousakis, P. S. Georgilakis, and G. N. Korres, “Numerical observability method for optimal phasor measurement units placement using recursive Tabu search method,” IET Generation, Transmission & Distribution, vol. 7, no, 4, pp. 347-356, 2013.
[19] H. Mori, “A GA-based method for optimizing topological observability index in electric power networks,” IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference, pp. 565-568, 1994.
[20] X. Bei, Y. J. Yoon, and A. Abur, “Optimal placement and utilization of phasor measurements for state estimation,” PSERC Publication, pp. 05-20, 2000.
[21] P. Chen, Z. Chen, and B. Bak-Jensen, “Probabilistic load flow: A review,” In Electric Utility Deregulation and Restructuring and Power Technologies, pp. 1586-1591, 2008.
[22] C. L. Su, “Probabilistic load-flow computation using point estimate method,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 1843-1851, 2005.
[23] Z. Wang, and F. L. Alvarado, “Interval arithmetic in power flow analysis,” IEEE Transactions on Power Systems, vol. 7, no. 3, pp. 1341-1349, 1992.
[24] W. G. Cochran, Sampling Techniques, 2nd ed., New York: Wiley,1977.
[25] R. Singh, B. C. Pal, and R. B. Vinter, “Measurement placement in distribution system state estimation,” IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 668-675, 2009.
[26] D. Bertsimas, and I. Popescu, “Optimal inequalities in probability theory: A convex optimization approach,” SIAM Journal on Optimization, vol. 15, no. 3, pp. 780-804, 2005.
[27] A. Primadianto, “Comparative Study of WLS Based Distribution System State Estimation,” Master Thesis, Department of Electrical Engineering National Sun Yat-sen University, 2015.
[28] M. E. Baran, and A. W. Kelley, “State estimation for real-time monitoring of distribution systems,” IEEE Transactions on Power Systems, vol. 9, no. 3, pp. 1601-1609, 1994.
[29] T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms” MIT Press, 2009.
[30] W. T. Lin, “Bad Data Detection in Distribution System State Estimation.” Master Thesis, Department of Electrical Engineering National Sun Yat-sen University, 2015
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code