請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80900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 | Cheng-Jhe Wu | en |
| dc.contributor.author | 吳承哲 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:21:08Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:21:08Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-23 | |
| dc.identifier.citation | 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, Wales: UPEC. Allam, F. 2020. 'Protecting Overhead Transmission Lines (OHL) from Hot Spots Using Dynamic Line Rating (DLR) Calculations,' 2020 12th International Conference on Electrical Engineering (ICEENG), pp. 44-49 Aznarte, J. L., and Siebert, N. 2017. Dynamic line rating using numerical weather predictions and machine learning: A case study. IEEE Transactions on Power Delivery, 32(1), 335-343. Bates, J. M. and Granger, C. W. J. 1969. 'The combination of forecasts', Oper. Res. Soc., vol. 20, no. 4, pp. 451-468 Burke, D. J. and O’Malley, M. J. 2011. 'A study of principal component analysis applied to spatially distributed wind power', IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2084-2092, Nov. Chittock, L. M., Strickland, D. and Harrap, C. 2015. 'Weather forecasting to predict practical dynamic asset rating of overhead lines,' IET International Conference on Resilience of Transmission and Distribution Networks (RTDN) 2015, pp. 1-6 Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259. CIGRE, WG22. 12. 1997. The thermal behavior of overhead conductors. ELECTRA (144): 107-125. Fan, S. and Chen, L. 2006. 'Short-term load forecasting based on an adaptive hybrid method', IEEE Trans. Power Syst., vol. 21, no. 1, pp. 392-401, Feb. FERNÁNDEZ DE SEVILLA, S., GONZALEZ, G., JUBERIAS, G., MARTÍNEZ, L., ESCRIBANO, M., IGLESIAS, J., ALBI, P., BÚRDALO, U., MUÑÍZ, Á. and KWIK, S. 2014. Dynamic Assessment of Overhead Line Capacity for integrating Renewable Energy into the Transmission Grid. CIGRE. B2-207 Foss, S. D., Lin, S., Maraio, R. A., and Schrayshuen, H. N. M. P. C. 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. Hahnloser, R. (1998). On the piecewise analysis of networks of linear threshold neurons. Neural Networks, 11(4), 691-697. Hall, J. F., and Deb, A. K. 1988. Prediction of overhead transmission line ampacity by stochastic and deterministic models. IEEE Transactions on Power Delivery 3(2): 789-800. Haq, M. R., Ni, Z. 2019. A new hybrid model for short-term electricity load forecasting. IEEE Access, 7, 125413-125423. Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K.. 'Extreme learning machine: Theory and applications.' Neurocomputing 70, no. 1-3 (2006), 489-501. Huang, R., Pilgrim, J. A., Lewin, P. L. and Payne, D. 2013. 'Dynamic cable ratings for smarter grids,' IEEE PES ISGT Europe 2013, pp. 1-5 Heckenbergerova, J., Musilek, P. and Filimonenkov, K. 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. Hochreiter, S. and Schmidhuber, J. 1997. Long Short-term Memory. Neural Computation. 9(8): 1735-1780. Hosek, J., Musilek, P., Lozowski, E., and Pytlak, P. 2011. Effect of time resolution of meteorological inputs on dynamic thermal rating calculations. IET Generation, Transmission Distribution 5(9): 941-947. IEEE Std. 738-2012 (Revision of IEEE Std. 738-2006). 2012. IEEE standard for calculating the current-temperature of bare overhead conductors. Kazerooni, A. K., Mutale, J., Perry, M., Venkatesan, S., and Morrice, D. 2011. Dynamic thermal rating application to facilitate wind energy integration. In 'Proc. 2011 PowerTech IEEE Trondheim', 1-7. Trondheim, Norway: IEEE. Kim, D. M., Cho, J. M., Lee, H. S., Jung, H. S., and Kim, J. O. 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. Kim, S. D. and Morcos, M. M. 2013. An Application of Dynamic Thermal Line Rating Control System to Up-Rate the Ampacity of Overhead Transmission Lines. IEEE Trans. Power Delivery. 28(2): 1231-1232. Kong, X., Li, C., Zheng, F., and Wang, C. 2020. Improved deep belief network for short-term load forecasting considering demand-side management. IEEE Transactions on Power Systems, 35(2), 1531-1538. Koprinska, I., Wu, D. and Wang, Z. 2018. 'Convolutional neural networks for energy time series forecasting', Proc. Int. Joint Conf. Neural Netw. (IJCNN), pp. 1-8, Jul. Lv, P., Liu, S., Yu, W., Zheng, S., and Lv, J. 2020. EGA-STLF: A hybrid short-term load forecasting model. IEEE Access, 8, 31742-31752. Michi, L., Carlini, E. M., Migliori, M., Palone, F. and Lauria, S. 2019. 'Uprating studies for a 230 kV-50 Hz Overhead Line,' 2019 IEEE Milan PowerTech, pp. 1-6 Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E. and Damborg, M. J. 1991. 'Electric load forecasting using an artificial neural network', IEEE Trans. Power Syst., vol. 6, no. 2, pp. 442-449, May. Rafi, S. H., Nahid-Al-Masood, Deeba, S. R., Hossain, E. 2021. A short-term load forecasting method using integrated CNN and LSTM network. IEEE Access, 9, 32436-32448. Ramachandran, P., and V. Vittal. 2006. On-line monitoring of sag in overhead transmission lines with leveled spans. In “IEEE Power Symposium, 2006. NAPS 2006. 38th North American”, 405-409, Carbondale, USA: IEEE. Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. Fernandez Martinez, R., Alberdi, R., Fernandez, E., Albizu, I. and Bedialauneta, M. T. 2019. 'Improvement of safety operating conditions in overhead conductors based on ampacity modeling using artificial neural networks,' 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1-5 Safari, N., Mazhari, S. M., Chung, C. Y. and Ko, S. B., 'Secure Probabilistic Prediction of Dynamic Thermal Line Rating,' in Journal of Modern Power Systems and Clean Energy Salinas, E., Abbott, L. F. 1996. A model of multiplicative neural responses in parietal cortex. Proceedings of the National Academy of Sciences, 93(21): 11956-11961. Schmidt, N. P. 1999. Comparison between IEEE and CIGRE ampacity standards. IEEE Transactions on Power Delivery 14(4): 1555-1559. Staszewski, L., and Rebizant, W. 2010. The Differences between IEEE and CIGRE Heat Balance Concepts for Line Ampacity Considerations. In 'Proc. Modern Electric Power Systems (MEPS), 2010 Proceedings of the International Symposium'', 1-4. Wroclaw, POLAND: MEPS. Shaker, H., Fotuhi-Firuzabad, M., and Aminifar, F.. 2012. Fuzzy dynamic thermal rating of transmission lines. IEEE Transactions on Power Delivery 27(4): 1885-1892. Xu, F. 2014. Dynamic thermal rating monitoring and analysis for overhead lines. Master’s thesis. Manchester, UK: University of Manchester. Yan, K., Li, W., Ji, Z., Qi, M., Du, Y. 2019. A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access, 7, 157633-157642. Zhang, R., Lan, Y., Huang, G.-B., Xu, Z.-B. and Soh, Y. C. 2013. 'Dynamic extreme learning machine and its approximation capability', IEEE Trans. Cybern., vol. 43, no. 6, pp. 2054-2065, Dec. Zhang, P., Shao, M., Leoni, A. R., Ramsay, D. H., and Graham, M.. 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/123456789/80900 | - |
| dc.description.abstract | "隨著電力需求與日俱增，超高壓電網所傳輸的負載也越來越重，準確的負載預測能使電力調度者做出關鍵的決策。此外，由於再生能源的興起其電力併網時可能會衝擊目前的電源調度模式，隨著發電不確定性的增加，其重要性進一步提高。目前台灣電力是根據靜態熱容量(Static thermal rating, STR)為參考做調度，但這會顯得過於保守且不夠靈活有效益。近年來被認為可能解決這個問題的技術為動態熱容量(Dynamic thermal rating , DTR)。DTR利用天氣資訊來估算架空輸電線的載流容量，是協助智慧電網進行規劃與決策的有效工具，如果能夠預測出未來數小時的載流變化，不僅能在不犧牲輸電安全的情況下提升輸電效益並且能夠提早處理異常載流的情況發生。因此，本研究提出了三種不同的混合預測模型比較，分別為Recurrent neural network (RNN)、Long Short-Term Memory (LSTM) 與 Gated Recurrent Unit (GRU)，並搭配Extreme Learning Machine(ELM)選出其準確度較佳的混合模型作為安全裕度的預測模型，最後將其預測結果搭配本研究提出的電力調度策略應用於五種特別挑選出來的案例，並以輸電線垂度和敏感度分析來評估其可靠性。本研究結果證實，藉由提出的策略限制下能在安全無疑的增加輸電線的負載，更重要的是能夠應付多種可能會遇到的負載變動，並且藉由敏感度分析能夠找出敏感度相對較高的跨距，降低決策者陷入調度盲點使調整負載時發生不可逆的風險。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:21:08Z (GMT). No. of bitstreams: 1 U0001-2109202117115500.pdf: 5380244 bytes, checksum: 32cd79b03402bb4c8f5e36e3f703d354 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 i 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 and propose 3 1.3 Thesis organization 4 Chapter 2 Literature Review 6 2.1 Dynamic thermal rating 6 2.2 Standards for Dynamic Thermal Rating 7 2.3 Dynamic thermal rating prediction 9 2.4 Dispatching strategy 10 Chapter 3 Materials and Methods 13 3.1 Meteorological grid data 13 3.2 IEEE standard 738-2012 14 3.3 Experimental framework 22 3.4 Prediction of safety margin 24 3.4.1 The process of predicting a safety margin 24 3.4.2 Prediction model framework 26 3.4.2 Recurrent neural network 29 3.4.4 Long short-term memory 30 3.4.5 Gate recurrent unit 32 3.4.6 Extreme learning machine 33 3.4.7 Criteria for evaluation 35 3.5 Dispatching strategy 37 3.5.1 New rules for safety margin restriction 37 3.5.2 Sensitivity of a tower span responding to load changes 39 Chapter 4 Results and Discussion 41 4.1 Dataset 41 4.2 Prediction model selection 44 4.2.1 ELM and RNN combination 45 4.2.2 ELM and RNN combination 46 4.2.3 ELM and RNN combination 48 4.2.4 Comparison of three different conbined models 49 4.3 Experimental design 54 4.3.1 Case 1 55 4.3.2 Case 2 58 4.3.3 Case 3 61 4.3.4 Case 4 64 4.3.5 Case 5 68 4.3.6 Sensitivity analysis and summary 71 Chapter 5 Conclusions and Future Work 82 5.1 Conclusions 75 5.2 Future work 75 References 77 Appendix A Selected list of symbols 89 | |
| dc.language.iso | en | |
| dc.subject | 敏感度分析 | zh_TW |
| dc.subject | 智慧電網 | zh_TW |
| dc.subject | 動態熱容量 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 安全裕度預測 | zh_TW |
| dc.subject | Smart grid | en |
| dc.subject | Safety margin prediction | en |
| dc.subject | Deep learning | en |
| dc.subject | Dynamic thermal rating | en |
| dc.subject | Sensitivity analysis | en |
| dc.title | 基於深度學習之超高壓輸電線安全裕度預測及調度策略擬定 | zh_TW |
| dc.title | Safety Margin Prediction and Ampacity Dispatch Planning for EHV Transmission Line Based on Deep Learning | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周呈霙(Hsin-Tsai Liu),王永鐘(Chih-Yang Tseng),蕭瑛東,王人正 | |
| dc.subject.keyword | 智慧電網,動態熱容量,深度學習,安全裕度預測,敏感度分析, | zh_TW |
| dc.subject.keyword | Smart grid,Dynamic thermal rating,Deep learning,Safety margin prediction,Sensitivity analysis, | en |
| dc.relation.page | 89 | |
| dc.identifier.doi | 10.6342/NTU202103267 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-23 | |
| 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 | |
|---|---|---|---|
| U0001-2109202117115500.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 5.25 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。