請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73894完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
| dc.contributor.author | Shih-Syuan Hong | en |
| dc.contributor.author | 洪士軒 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:13:03Z | - |
| dc.date.available | 2021-02-20 | |
| dc.date.copyright | 2021-02-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-02-04 | |
| dc.identifier.citation | 台灣電力公司。2018。歷年停限電次數。台北：台灣電力公司。網址：https://www.taipower.com.tw/tc/chart_m/d01_%E7%94%A8%E6%88%B6%E8%B3%87%E8%A8%8A_%E9%99%90%E9%9B%BB%E7%9F%A5%E8%AD%98%E8%88%87%E8%B3%87%E8%A8%8A_%E6%AD%B7%E5%B9%B4%E5%81%9C%E9%99%90%E9%9B%BB%E6%AC%A1%E6%95%B8.html。上網日期：2018-06-25。 行政院。2017。815 停電事故行政調查專案報告。台北：行政院。網址：https://www.ey.gov.tw/File/63C242F98FB519ED?A=C。上網日期：2018-06-25。 徐業良。1995。工程最佳化設計。台北：國立編譯館。 黃郁文、黃渡根、林俊傑、李智強。2007。輸變電系統智慧化監控。中華技術期刊 74: 98-111。 劉盈昌。2014。輸電線路動態增容技術。台北：公務出國報告資訊網。網址：https://report.nat.gov.tw/ReportFront/ReportDetail/detail?sysId=C10300180。上網日期：2018-06-23。 顧欣怡、王信凱、鄭安孺、高慧萱、陳怡彣、呂國臣。2011。高解析度網格點氣象分析系統。出自'建國百年天氣分析預報與地震測報研討會論文彙編'，259-263。台北：中央氣象局。 Abbott, S., S. Abdelkader, L. Bryans, and D. Flynn. 2010. Experimental validation and comparison of IEEE and CIGRE dynamic line models. . In 'Proc. 45th International Universities Power Engineering Conference (UPEC) ', 1-5. Cardiff, UK: IEEE. Black, J., S. Connor, and J. Colandairaj. 2010. Planning network reinforcements with dynamic line ratings for overhead transmission lines. In 'Proc. 45th International Universities Power Engineering Conference (UPEC) ', 1-6. Cardiff, UK: IEEE. Baumgartner, U., C. Magele, and W Renhart. 2004. Pareto optimality and particle swarm optimization. IEEE Transactions on Magnetics 40(2): 1172-1175. Berkooz, G., P. Holmes, and J. L. Lumley. 1993. The proper orthogonal decomposition in the analysis of turbulent flows. Annual Review of Fluid Mechanics 25(1): 539-575. Callahan, P. M., and D. A. Douglass. 1988. An experimental evaluation of a thermal line uprating by conductor temperature and weather monitoring. IEEE Transactions on Power Delivery 3(4): 1960-1967. Chan, Y. A., M. S. Liao, C. H. Wang, Y. C. Lee, and J. A. Jiang. 2015. Image repainted method of overlapped leaves for orchid leaves area estimation. In 'Proc. of the 2015 9th International Conference on Sensing Technology (ICST 2015)', 205–210. Auckland, New Zealand: IEEE. CIGRE, WG22. 12. 1997. The thermal behavior of overhead conductors. ELECTRA (144): 107-125. Conductors, O. E. 1995. Calculation methods for stranded bare conductors. IEC Standard, 61597. Coello, C. A. C., G. T. Pulido, and M. S. Lechuga. 2004. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3): 256-279. de Sousa Xavier, A. M., M. D. B. C. Freitas, and R. M. de Sousa Fragoso. 2015. Management of Mediterranean forests—A compromise programming approach considering different stakeholders and different objectives. Forest Policy and Economics 57: 38-46. Del Valle, Y., G. K. Venayagamoorthy, S. Mohagheghi, J. C. Hernandez, and R. G. Harley. 2008. Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation 12(2): 171-195. Dowell, E., K. Hall, J. Thomas, R. Florea, B. Epureanu, and J. Heeg. 1999. Reduced order models in unsteady aerodynamics. In 'Proc. 40th Structures, Structural Dynamics, and Materials Conference and Exhibit', 1261. St. Louis, MO, U.S.A. Engelbrecht, A. P. 2006. Fundamentals of computational swarm intelligence. U.S.A.: John Wiley Sons. Eberhart, R., and J. Kennedy. 1995. A new optimizer using particle swarm theory. In 'Proc. Sixth International Symposium on Micro Machine and Human Science', 39-43. Nagoya, Japan: IEEE. Foss, S. D., S. Lin, R. A. Maraio, and H. N. M. P. C. Schrayshuen. 1988. Effect of variability in weather conditions on conductor temperature and the dynamic rating of transmission lines. IEEE Transactions on Power Delivery 3(4): 1832-1841. Hall, J. F., and A. K. Deb. 1988. Prediction of overhead transmission line ampacity by stochastic and deterministic models. IEEE Transactions on Power Delivery 3(2): 789-800. Heckenbergerova, J., P. Musilek, and K. Filimonenkov. 2011. Assessment of seasonal static thermal ratings of overhead transmission conductors. In 'Proc. Power and Energy Society General Meeting', 1-8. Detroit, MI, U.S.A.: IEEE. Hosek, J., P. Musilek, E. Lozowski, and P. Pytlak. 2011. Effect of time resolution of meteorological inputs on dynamic thermal rating calculations. IET Generation, Transmission Distribution 5(9): 941-947. Hu, X., and R. Eberhart. 2002. Multiobjective optimization using dynamic neighborhood particle swarm optimization. In 'Proc. IEEE Congress on Evolutionary Computation (CEC 2002)', vol. 2: 1677-1681. Honolulu, HI, U.S.A.: IEEE. IEEE Std. 738-2012 (Revision of IEEE Std. 738-2006). 2012. IEEE standard for calculating the current-temperature of bare overhead conductors. Jiang, J. A., Y. T Liang, C. P. Chen, X. Y. Zheng, C. L. Chuang, and C. H. Wang. 2016. On dispatching line ampacities of power grids using weather-based conductor temperature forecasts. IEEE Transactions on Smart Grid 9(1): 406-415. Jiang, J. A., J. J. Wan, X. Y. Zheng, C. P. Chen, C. H. Lee, L. K. Su, and W. C. Huang. 2018. A novel weather information-based optimization algorithm for thermal sensor placement in smart grid. IEEE Transactions on Smart Grid 9(2): 911-922. Jian-Hua, L., Y. Rong-Hua, and S. Shui-Hua. 2011. The analysis of binary particle swarm optimization. Journal of Nanjing University (Natural Sciences) 47(5): 504-514. Kazerooni, A. K., J. Mutale, M. Perry, S. Venkatesan, and D. Morrice. 2011. Dynamic thermal rating application to facilitate wind energy integration. In 'Proc. 2011 PowerTech IEEE Trondheim', 1-7. Trondheim, Norway: IEEE. Ke, W. C., B. H. Liu, and M. J. Tsai. 2007. Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP-complete. IEEE Transactions on Computers 56(5): 710-715. Kennedy, J., and R. C. Eberhart. 1997. A discrete binary version of the particle swarm algorithm. In 'Proc. IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation', 4104-4108. Orlando, FL, U.S.A.: IEEE. Kulkarni, R. V., and G. K. Venayagamoorthy. 2011. Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41(2): 262-267. Khanesar, M. A., M. Teshnehlab, and M. A. Shoorehdeli. 2007. A novel binary particle swarm optimization. In 'Proc. Mediterranean Conference on Control Automation (MED'07)', 1-6. Athens, Greece: IEEE. Kim, D. M., J. M. Cho, H. S. Lee, H. S. Jung, and J. O. Kim. 2006. Prediction of dynamic line rating based on assessment risk by time series weather model. In 'International Conference on Probabilistic Methods Applied to Power Systems', 1-7. Stockholm, Sweden: IEEE. Krishnamurthy, P., and F. Khorrami. 2018. Optimal sensor placement for monitoring of spatial networks. IEEE Transactions on Automation Science and Engineering 15(1): 33-44. Khezri, S., K. Faez, and A. Osmani. 2010. Modified discrete binary PSO based sensor placement in WSN networks. In 'Proc. International Conference on Computational Intelligence and Communication Networks', 200-204. Bhopal, India: IEEE. Lindberg, E. 2011. The overhead line sag dependence on weather parameters and line current. Master’s dissertation. Uppsala, Sweden: Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences. Latiff, N. A., C. C. Tsimenidis, and B. S. Sharif. 2007. Performance comparison of optimization algorithms for clustering in wireless sensor networks. In 'Proc. IEEE International Conference on Mobile Adhoc and Sensor Systems', 1-4. Pisa, Italy: IEEE. Mois, G., S. Folea, and T. Sanislav. 2017. Analysis of three IoT-based wireless sensors for environmental monitoring. IEEE Transactions on Instrumentation and Measurement 66(8): 2056-2064. Marler, R. T., and J. S. Arora. 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26(6): 369-395. Mishra, C., K. D. Jones, A. Pal, and V. A. Centeno. 2016. Binary particle swarm optimisation-based optimal substation coverage algorithm for phasor measurement unit installations in practical systems. IET Generation, Transmission Distribution 10(2): 555-562. Mirjalili, S., and A. Lewis. 2013. S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm and Evolutionary Computation 9: 1-14. Minkel, J. R. 2008. The 2003 Northeast blackout--five years later. Scientific American 13. Musilek, P., J. Heckenbergerova, and M. M. I. Bhuiyan. 2012. Spatial analysis of thermal aging of overhead transmission conductors. IEEE Transactions on Power Delivery 27(3): 1196-1204. Morsly, Y., N. Aouf, M. S. Djouadi, and M. Richardson. 2012. Particle swarm optimization inspired probability algorithm for optimal camera network placement. IEEE Sensors Journal 12(5): 1402-1412. Moore, J. 1999. Application of particle swarm to multiobjective optimization. Technical report. Auburn, AL, U.S.A.: Auburn University, Department of Computer Science and Software Engineering. Naeem, M., U. Pareek, and D. C. Lee. 2012. Swarm intelligence for sensor selection problems. IEEE Sensors Journal 12(8): 2577-2585. Newman, A. J. 1999. Modeling and reduction with applications to semiconductor processing. Ph.D. dissertation. College Park, Maryland, U.S.A.: University of Maryland. Olazarri, J. G., A. J. Mazon, E. Fernandez, I. Albizu, and M. T. Bedialauneta. 2015. Analysis of different dynamic line rating system configurations in a distribution line. In '23rd International Conference on Electricity Distribution'. Lyon, France: CIRED. Pytlak, P., and P. Musilek. 2010. An intelligent weather-based system to support optimal routing of power transmission lines. In 'Proc. IEEE Electric Power and Energy Conference (EPEC)', 1-6. Halifax, NS, Canada: IEEE. Pareto, V. 1906. Manuale di economia politica. Societa Editrice Libraria. Parsopoulos, K. E., D. K. Tasoulis, and M. N. Vrahatis. 2004. Multiobjective optimization using parallel vector evaluated particle swarm optimization. In 'Proc. IASTED International Conference on Artificial Intelligence and Applications', vol. 2, 823-828. Innsbruck, Austria:Acta Press. Reyes-Sierra, M., and C. C. Coello. 2006. Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research 2(3): 287-308. Schmidt, N. P. 1999. Comparison between IEEE and CIGRE ampacity standards. IEEE Transactions on Power Delivery 14(4): 1555-1559. Shaker, H., M. Fotuhi-Firuzabad, and F. Aminifar. 2012. Fuzzy dynamic thermal rating of transmission lines. IEEE Transactions on Power Delivery 27(4): 1885-1892. Safdarian, A., M. Z. Degefa, M. Fotuhi-Firuzabad, and M. Lehtonen. 2015. Benefits of real-time monitoring to distribution systems: Dynamic thermal rating. IEEE Transactions on Smart Grid 6(4): 2023-2031. Shieh, C. S., V. O. Sai, Y. C. Lin, T. F Lee, T. T. Nguyen, and Q. D. Le. 2016. Improved node localization for WSN using heuristic optimization approaches. In 'Proc. International Conference on Networking and Network Applications (NaNA)', 95-98. Hakodate, Japan: IEEE. Sirovich, L. 1992. Analysis and management of large scientific databases. Int. J. Supercomp. Appl. 6: 50-68. Teh, J., C. M. Lai, and Y. H. Cheng. 2017. Impact of the real-time thermal loading on the bulk electric system reliability. IEEE Transactions on Reliability 66(4): 1110-1119. Wan, H., J. D. McCalley, and V. Vittal. 1999. Increasing thermal rating by risk analysis. IEEE Transactions on Power Systems 14(3): 815-828. Worden, K., and A. P. Burrows. 2001. Optimal sensor placement for fault detection. Engineering Structures 23(8): 885-901. Wang, L., X. Fu, J. Fang, H. Wang, and M. Fei. 2011. Optimal node placement in industrial wireless sensor networks using adaptive mutation probability binary particle swarm optimization algorithm. In 'Proc. Seventh International Conference on Natural Computation', vol.4, 2199-2203. Shanghai, China: IEEE. Xu, F. 2014. Dynamic thermal rating monitoring and analysis for overhead lines. Master’s thesis. Manchester, UK: University of Manchester. Yu, T. K., and J. H. Seinfeld. 1973. Observability and optimal measurement location in linear distributed parameter systems. International Journal of Control 18(4): 785-799. Yang, J., H. Zhang, Y. Ling, C. Pan, and W. Sun. 2014. Task allocation for wireless sensor network using modified binary particle swarm optimization. IEEE Sensors Journal 14(3): 882-892. Zeleny, M. 1973. Compromise programming. In 'Multiple criteria decision making'. California, U.S.A.: University of South Carolina Press. Zhang, P., M. Shao, A. R. Leoni, D. H. Ramsay, and M. Graham. 2008. Determination of static thermal conductor rating using statistical analysis method. In 'Proc. Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies', 1237-1243. Nanjing, China: IEEE. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73894 | - |
| dc.description.abstract | 本研究將風險管理評估應用於高壓輸電線上，並估算未來一天動態熱容量，提前得知可調配的載流量狀況。有鑑於輸電線線路導體溫度對於輸電安全及提升輸電效益的重要性，然而輸電線線路溫度及線路電流資訊現階段的作法皆由IEEE Std 738-2012標準規範的動態額定熱容量公式推估而來，其估計值的正確性攸關著輸電安全，是為輸電系統安全評估的一大重要課題。由氣象局提供的氣候資訊(風速及環境溫度)做為IEEE Std 738-2012標準規範中動態熱容量方法所推估出的估計值，其中環境溫度由過去一個月進行統計分析得出未來一天可能溫度範圍，風速則是透過機器學習方法由過去12小時風速得到未來3小時風速，再將此結果套入IEEE Std 738-2012標準規範可以得到未來一天動態熱容量範圍，再將最低點減去100安培，此值視為最大可調配額定值，結果顯示出此方法有95%不會造成輸電線損壞又能提高輸電容量，並且用導體溫度及輸電線垂度驗證此次提高電流並不會造成損壞。 為了最大程度減少危害，所以透過風險管理評估，將輸電線進行導體損壞概率的計算，進而準確判斷出損壞概率，提前作出對策。導體損壞概率主要使用應力模型及老化模型，並由韋伯分布函數將此兩模型結合，將經驗參數訓練完後，只需將導體溫度代入，即可得知損壞概率。本研究表明輸電線路上的導體溫度與氣象參數息息相關，甚至與其所能負載的電流容量皆有重大影響，在輸電安全評估上亦與氣候參數有著密不可分的關係。透過風險管理評估確保輸電安全性，透過風險管理評估可提前規劃未來狀況，避免因為錯估輸電額度導致安全事故產生。 | zh_TW |
| dc.description.abstract | In this research, risk management assessment is applied to high-voltage transmission lines, and the dynamic thermal rating of the next day is estimated, and the adjustable current carrying capacity is known in advance. Because of the importance of the conductor temperature of the transmission line to the safety of power transmission and the improvement of the efficiency of power transmission, the current practice of the temperature and line current information of the transmission line is derived from the dynamic rated thermal rating formula of the IEEE 738-2012 standard. The correctness of its estimated value is critical to the safety of power transmission and is an important subject for the safety assessment of power transmission systems. The climate information (wind speed and ambient temperature) provided by the Bureau of Meteorology is the estimated value estimated by the dynamic thermal rating method in the IEEE 738-2012 standard specification, in which the ambient temperature is statistically analyzed in the past month to obtain the possible temperature for the next day The wind speed is the wind speed in the next 3 hours from the wind speed in the past 12 hours through the machine learning method, and then the result is inserted into the IEEE 738-2012 standard specification to get the dynamic thermal rating range for the next day, and then the lowest point is subtracted by 100 amperes. This value is regarded as the maximum adjustable rating. The results show that 95% of this method will not cause damage to the transmission line and increase the transmission capacity, and the conductor temperature and the sag of the transmission line are used to verify that the current increase will not cause damage. To minimize the damage, the risk management assessment is used to calculate the probability of conductor damage to the transmission line, and then accurately determine the probability of damage, and make countermeasures in advance. The conductor damage probability mainly uses the stress model and the aging model, and the Weber distribution function combines these two models. After training the empirical parameters, just substitute the conductor temperature to know the damage probability. This study shows that the conductor temperature on the transmission line is closely related to the meteorological parameters, and even has a significant impact on the current capacity it can load. It is also closely related to the climatic parameters in the transmission safety assessment. Through the risk management assessment to ensure the safety of power transmission, the future situation can be planned to avoid the occurrence of safety accidents caused by the misestimation of the transmission quota. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:13:03Z (GMT). No. of bitstreams: 1 U0001-2701202118161000.pdf: 3435711 bytes, checksum: 8bd9f33b29fe8f01fdd1ae38b22db503 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 i 摘要 ii Abstract iii Table of Contents v List of Figures viii List of Tables xi Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 6 1.3 Organization 8 Chapter 2 Literature Review 9 2.1 EHV transmission Lines 11 2.2 Thermal rating 14 2.2.1 Static thermal rating 14 2.2.2 Dynamic thermal rating 16 2.2.3 Dynamic thermal rating standard 19 2.3 Wind speed prediction 21 2.3.1 Time series forecasting model 21 2.3.2 Overview of different methods for DTR prediction 23 2.4 Risk assessment 28 Chapter 3 Materials and Methods 32 3.1 Meteorological grid data 32 3.2 IEEE 738-2012 standard 35 3.2.2 Convection heat loss rate 38 3.2.3 Radiated heat loss rate 39 3.2.4 Rate of solar heat gain 40 3.2.5 Conductor Electrical Resistance 42 3.3 Machine Learning Methods utilized in this research 43 3.4 Measurement and prediction performance evaluation 46 3.4.1 R-square, r, and RMSE 47 3.5 Line Failure Probability Model 48 Chapter 4 Results and Discussion 50 4.1 Dynamic thermal rating estimation 50 4.1.1 Meteorological grid data over years 51 4.1.2 Wind speed forecast 57 4.1.3 Estimated results and scheduling suggestions 60 4.2 Early warning of line temperature and sag 65 4.2.1 The relationship between conductor temperature and current estimated by IEEE 738-2012 standard 65 4.2.2 Early warning judgment of line temperature and sag 67 4.3 Risk management assessment 68 4.3.1 Experience parameters of aging model 68 4.3.2 Failure probability calculation 69 Chapter 5 Conclusions 72 References 73 | |
| dc.language.iso | en | |
| dc.subject | 智慧電網 | zh_TW |
| dc.subject | 動態熱容量 | zh_TW |
| dc.subject | 輸電線溫度 | zh_TW |
| dc.subject | 風險管理評估 | zh_TW |
| dc.subject | 垂度 | zh_TW |
| dc.subject | sag | en |
| dc.subject | Dynamic thermal ratings | en |
| dc.subject | Transmission temperature | en |
| dc.subject | Smart grid | en |
| dc.subject | Risk management assessment | en |
| dc.title | 智慧電網高壓輸電線之動態熱容量估算與風險管理評估 | zh_TW |
| dc.title | Dynamic Thermal Rating Estimation and Risk Management Assessment of HV transmission lines in Smart Grid | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周呈霙(Cheng-Ying Zhou),王永鐘(Yung-Chung Wang),蕭瑛東(Ying-Tung Hsiao),李建興(Chien-Hsing Lee) | |
| dc.subject.keyword | 智慧電網,動態熱容量,輸電線溫度,風險管理評估,垂度, | zh_TW |
| dc.subject.keyword | Smart grid,Dynamic thermal ratings,Transmission temperature,Risk management assessment,sag, | en |
| dc.relation.page | 80 | |
| dc.identifier.doi | 10.6342/NTU202100216 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2021-02-05 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2701202118161000.pdf 未授權公開取用 | 3.36 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。