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題名 以空間資訊技術建立電網脆弱度自然因素風險評估模型之研究
Research on Establishing Power Grid Vulnerability Model
作者 許又婕
Hsu, Yu-Chieh
貢獻者 甯方璽
許又婕
Hsu, Yu-Chieh
關鍵詞 電網脆弱度
永續發展目標
地理資訊系統
隨機森林
Power Grid Vulnerability
Sustainable Development Goals
Geographic Information System
Random Forest
日期 2024
上傳時間 5-Aug-2024 14:14:45 (UTC+8)
摘要 本研究以空間資訊技術建立電網脆弱度自然因素風險評估模型，評估電力系統對抗自然災害的能力。由於氣候變遷與極端天氣事件頻繁發生，對電網構成重大威脅，因此相關單位需要有效的風險評估方法，以提前規劃與預防災害的發生。本研究綜合運用地理資訊系統（GIS），結合自然災害因子和電網結構資訊，進行電網脆弱度分析與風險評估。 模型評估首先利用空間分析技術評估電網設施的脆弱性，考量因素包括斷層、地質敏感帶、淹水潛勢區和山崩等自然因子。並整合各類自然因素風險，計算出脆弱度指數，繪製詳細的脆弱度地圖。 研究結果顯示，隨機森林算法能夠有效地辨識影響電網脆弱度的主要自然因子，並計算其權重。本模型能夠劃分出脆弱高的區域，為相關單位提供參考，以便規劃分散式電網、提升防災減災能力，並實現聯合國永續發展目標（SDGs）中的第7項「確保所有的人都可取得負擔得起、可靠、永續及現代的能源」（Affordable and Clean Energy），改變現有集中式電網面對極端氣候的不穩定性，並提高再生能源電網的比例。
This study employs spatial information technology to establish a natural factor risk assessment model for power grid vulnerability, evaluating the ability of power systems to withstand natural disasters. Due to the frequent occurrence of climate change and extreme weather events, which pose significant threats to power grids, relevant authorities require effective risk assessment methods to plan and prevent disasters in advance. This study comprehensively utilizes Geographic Information System (GIS) technology, integrating natural disaster factors and power grid structure information to analyze power grid vulnerability and assess risk. First, the model assessment uses spatial analysis techniques to evaluate the vulnerability of power grid facilities, considering factors such as faults, geologically sensitive area, potential debris flow torrent, and landslides. The vulnerability index is calculated by analyzing natural factors, resulting in a vulnerability map. The results indicate that the random forest algorithm can effectively identify the major natural factors influencing power grid vulnerability and calculate their weights. This model can delineate high-vulnerability areas, providing reference for the authorities to plan distributed power grids, enhance disaster prevention and risk mitigation, and achieve Sustainable Development Goal 7 (SDG7) calls for “affordable, reliable, sustainable and modern energy for all”. It will improve the stability of centralized power grids in the face of extreme weather and increase the proportion of renewable energy grids.
參考文獻 中文參考文獻 何春蓀（1990）。普通地質學, 國立編譯館主編: 五南圖書出版有限公司。 余美儀（2017）。山區輸電鐵塔遇天然災害類型之危害分析探討［未出版之碩士論文］。國立雲林科技大學營建工程系。 吳秉昇、陳宇軒、王冠棋（2021）。雲林地層下陷之脆弱度評估與地理加權迴歸分析。中國地理學會會刊，（68），25-42。https://doi.org/10.29972/bgsc.202112_(68).0002 周桂田、許志義（2022）。我國電力供需問題及能源轉型策略。臺灣大學風險社會與政策研究中心。 梁啟源、劉致峻、鄭睿合、呂易恂、郭博堯、Liang, C.-y.、Liu, C.-c.、Jheng, R.-h.、Lu, Y.-h.、Kuo, P.-y.（我國能源脆弱度分析與因應策略建議。臺灣能源期刊，4卷卷4期期，頁361-400。 莊敏賢、何國謙、林俐玲（2006）。高壓電塔塔基安全評估之研究。坡地防災學報，5（2），1-14。https://doi.org/10.29995/jshr.200612.0001 許博鈞（2017）。應用台灣地震損失評估系統探討嘉義市歷史地震及鄰近活動斷層之地震災害與風險之研究［未出版之碩士論文］。國立成功大學地球科學系碩士在職專班。 陳柏榮、洪景山（2015）。臺灣電力公司閃電資料特徵分析。大氣科學，43（4），285-300。 陳磊、鄧欣怡、陳紅坤、石晶（2022）。電力系統韌性評估與提升研究綜述。電力系統保護與控制，50（13），11-22。 黃靜宜（2016）。棲蘭野生動物重要棲息環境之脆弱度評估。台灣生物多樣性研究，18（1），93-108。 趙文衡（2018）。國際電網韌性發展趨勢對我國能源合作的啟示。107年度強化APEC參與及雙邊與多邊能源國際合作之推動與研析。台灣經濟研究院 鄭國鑫, 雷., 王湘, 羅小春（2020）。地震災害模擬及配電網的風險評估。電工技術學報，35（24），5218-5226。https://doi.org/10.19595/j.cnki.1000-6753.tces.191495 魏震波、劉俊勇、朱國俊、朱康、劉友波、王民昆（2010）。基於可靠性加權拓撲模型下的電網脆弱性評估模型。電工技術學報，25（8），131-137。   英文參考文獻 Abedi, A., Gaudard, L., & Romerio, F. (2019). Review of major approaches to analyze vulnerability in power system. Reliability Engineering & System Safety, 183, 153-172. https://doi.org/10.1016/j.ress.2018.11.019 Baldick, R., Chowdhury, B., Dobson, I., Zhaoyang, D., Bei, G., Hawkins, D., Zhenyu, H., Manho, J., Janghoon, K., Kirschen, D., Stephen, L., Fangxing, L., Juan, L., Zuyi, L., Chen-Ching, L., Xiaochuan, L., Mili, L., Miller, S., Nakayama, M., . . . Xiaoping, Z. (2009, 15-18 March 2009). Vulnerability assessment for cascading failures in electric power systems. 2009 IEEE/PES Power Systems Conference and Exposition, Bernstein, A., Bienstock, D., Hay, D., Uzunoglu, M., & Zussman, G. (2012). Sensitivity analysis of the power grid vulnerability to large-scale cascading failures. ACM SIGMETRICS Performance Evaluation Review, 40, 33-37. https://doi.org/10.1145/2425248.2425256 Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324 Chang, L., & Wu, Z. (2011). Performance and reliability of electrical power grids under cascading failures. International Journal of Electrical Power & Energy Systems, 33(8), 1410-1419. https://doi.org/10.1016/j.ijepes.2011.06.021 Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social Vulnerability to Environmental Hazards. Social Science Quarterly, 84(2), 242-261. http://www.jstor.org/stable/42955868 Disha, R. A., & Waheed, S. (2022). Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique. Cybersecurity, 5(1), 1. https://doi.org/10.1186/s42400-021-00103-8 Ghorani, R., Fattaheian-Dehkordi, S., Farrokhi, M., Fotuhi-Firuzabad, M., & Lehtonen, M. (2021). Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide. IEEE Access, 9, 80300-80309. https://doi.org/10.1109/access.2021.3084368 Johansson, J., Hassel, H., & Zio, E. (2013). Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems. Reliability Engineering & System Safety, 120, 27-38. https://doi.org/10.1016/j.ress.2013.02.027 Kattaa, B., Al-Fares, W., & Al Charideh, A. R. (2010). Groundwater vulnerability assessment for the Banyas Catchment of the Syrian coastal area using GIS and the RISKE method. J Environ Manage, 91(5), 1103-1110. https://doi.org/10.1016/j.jenvman.2009.12.008 Li, M., Hou, H., Yu, J., Geng, H., Zhu, L., Huang, Y., Li, X., & Zhang, X.-S. (2021). Prediction of Power Outage Quantity of Distribution Network Users under Typhoon Disaster Based on Random Forest and Important Variables. Mathematical Problems in Engineering, 2021, 1-14. https://doi.org/10.1155/2021/6682242 Li, X., Wang, Y., Basu, S., Kumbier, K., & Yu, B. (2019). A debiased MDI feature importance measure for random forests. Advances in Neural Information Processing Systems, 32. Nazemi, M., & Dehghanian, P. (2020). Seismic-Resilient Bulk Power Grids: Hazard Characterization, Modeling, and Mitigation. IEEE Transactions on Engineering Management, 67(3), 614-630. https://doi.org/10.1109/tem.2019.2950669 Ouyang, M., Pan, Z., Hong, L., & Zhao, L. (2014). Correlation analysis of different vulnerability metrics on power grids. Physica A: Statistical Mechanics and its Applications, 396, 204-211. https://doi.org/10.1016/j.physa.2013.10.041 Panteli, M., Pickering, C., Wilkinson, S., Dawson, R., & Mancarella, P. (2016). Power system resilience to extreme weather: Fragility modeling, probabilistic impact assessment, and adaptation measures. IEEE Transactions on Power Systems, 32(5), 3747-3757. Papathoma-Köhle, M., Schlögl, M., & Fuchs, S. (2019). Vulnerability indicators for natural hazards: an innovative selection and weighting approach. Scientific Reports, 9(1), 15026. https://doi.org/10.1038/s41598-019-50257-2 Parrish, D. E. (1991). Lightning-caused distribution circuit breaker operations. IEEE Transactions on Power Delivery, 6(4), 1395-1401. https://doi.org/10.1109/61.97669 Rocchetta, R. (2022). Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics. Renewable and Sustainable Energy Reviews, 159. https://doi.org/10.1016/j.rser.2022.112185 Rosas-Casals, M. (2010). Power grids as complex networks. Topology and fragility. COMPENG 2010 - Complexity in Engineering, Scholz, M. (2006). Approaches to analyse and interpret biological profile data. Shahpari, A., Khansari, M., & Moeini, A. (2019). Vulnerability analysis of power grid with the network science approach based on actual grid characteristics: A case study in Iran. Physica A: Statistical Mechanics and its Applications, 513, 14-21. https://doi.org/https://doi.org/10.1016/j.physa.2018.08.059 Subramaniam, P., & Kaur, M. J. (2019). Review of Security in Mobile Edge Computing with Deep Learning 2019 Advances in Science and Engineering Technology International Conferences (ASET), Sui, X., Hu, M., Wang, H., & Zhao, L. (2022). Measurement of Coastal Marine Disaster Resilience and Key Factors with a Random Forest Model: The Perspective of China’s Global Maritime Capital. Water, 14(20). https://doi.org/10.3390/w14203265 Thornes, J. E. (2002). IPCC, 2001: Climate change 2001: impacts, adaptation and vulnerability, Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by J. J. McCarthy, O. F. Canziani, N. A. Leary, D. J. Dokken and K. S. White (eds). Cambridge University Press, Cambridge, UK, and New York, USA, 2001. No. of pages: 1032. Price: £34.95, ISBN 0-521-01500-6 (paperback), ISBN 0-521-80768-9 (hardback). International Journal of Climatology, 22(10), 1285-1286. https://doi.org/https://doi.org/10.1002/joc.775 Tingting, D., Junyong, L., Zhenbo, W., & Ye, C. (2010). Analysis of power system vulnerability based on complex network theory. 现代电力, 27(1), 56-60. Vanzi, I. (1996). Seismic reliability of electric power networks: methodology and application. Structural Safety, 18(4), 311-327. https://doi.org/https://doi.org/10.1016/S0167-4730(96)00024-0 Waseem, M., & Manshadi, S. D. (2020). Electricity grid resilience amid various natural disasters: Challenges and solutions. The Electricity Journal, 33(10). https://doi.org/10.1016/j.tej.2020.106864 Xie, B., Tian, X., Kong, L., & Chen, W. (2021). The Vulnerability of the Power Grid Structure: A System Analysis Based on Complex Network Theory. Sensors, 21(21), 7097. https://www.mdpi.com/1424-8220/21/21/7097 Yang, Q., Yin, S., Li, Q., & Li, Y. (2022). Analysis of electricity consumption behaviors based on principal component analysis and density peak clustering. Concurrency and Computation: Practice and Experience, 34(21). https://doi.org/10.1002/cpe.7126 Zhu, D., Wang, H., Wang, R., Duan, J., & Bai, J. (2022). Identification of Key Nodes in a Power Grid Based on Modified PageRank Algorithm. Energies, 15(3). https://doi.org/10.3390/en15030797
描述 碩士
國立政治大學
地政學系
111257001
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111257001
資料類型 thesis
dc.contributor.advisor 甯方璽zh_TW
dc.contributor.author (Authors) 許又婕zh_TW
dc.contributor.author (Authors) Hsu, Yu-Chiehen_US
dc.creator (作者) 許又婕zh_TW
dc.creator (作者) Hsu, Yu-Chiehen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 14:14:45 (UTC+8)-
dc.date.available 5-Aug-2024 14:14:45 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 14:14:45 (UTC+8)-
dc.identifier (Other Identifiers) G0111257001en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152824-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 111257001zh_TW
dc.description.abstract (摘要) 本研究以空間資訊技術建立電網脆弱度自然因素風險評估模型，評估電力系統對抗自然災害的能力。由於氣候變遷與極端天氣事件頻繁發生，對電網構成重大威脅，因此相關單位需要有效的風險評估方法，以提前規劃與預防災害的發生。本研究綜合運用地理資訊系統（GIS），結合自然災害因子和電網結構資訊，進行電網脆弱度分析與風險評估。 模型評估首先利用空間分析技術評估電網設施的脆弱性，考量因素包括斷層、地質敏感帶、淹水潛勢區和山崩等自然因子。並整合各類自然因素風險，計算出脆弱度指數，繪製詳細的脆弱度地圖。 研究結果顯示，隨機森林算法能夠有效地辨識影響電網脆弱度的主要自然因子，並計算其權重。本模型能夠劃分出脆弱高的區域，為相關單位提供參考，以便規劃分散式電網、提升防災減災能力，並實現聯合國永續發展目標（SDGs）中的第7項「確保所有的人都可取得負擔得起、可靠、永續及現代的能源」（Affordable and Clean Energy），改變現有集中式電網面對極端氣候的不穩定性，並提高再生能源電網的比例。zh_TW
dc.description.abstract (摘要) This study employs spatial information technology to establish a natural factor risk assessment model for power grid vulnerability, evaluating the ability of power systems to withstand natural disasters. Due to the frequent occurrence of climate change and extreme weather events, which pose significant threats to power grids, relevant authorities require effective risk assessment methods to plan and prevent disasters in advance. This study comprehensively utilizes Geographic Information System (GIS) technology, integrating natural disaster factors and power grid structure information to analyze power grid vulnerability and assess risk. First, the model assessment uses spatial analysis techniques to evaluate the vulnerability of power grid facilities, considering factors such as faults, geologically sensitive area, potential debris flow torrent, and landslides. The vulnerability index is calculated by analyzing natural factors, resulting in a vulnerability map. The results indicate that the random forest algorithm can effectively identify the major natural factors influencing power grid vulnerability and calculate their weights. This model can delineate high-vulnerability areas, providing reference for the authorities to plan distributed power grids, enhance disaster prevention and risk mitigation, and achieve Sustainable Development Goal 7 (SDG7) calls for “affordable, reliable, sustainable and modern energy for all”. It will improve the stability of centralized power grids in the face of extreme weather and increase the proportion of renewable energy grids.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究架構 4 第二章 文獻回顧 5 第一節 臺灣電力系統結構 5 第二節 脆弱度之定義與種類 7 第三節 電網脆弱度指標 8 第四節 自然災害因子 17 第五節 脆弱度重要性分析方法 21 第三章 研究方法 25 第一節 研究區域 25 第二節 研究資料 26 第三節 研究設計與流程 31 第四章 成果與分析 34 第一節 脆弱度因子分析成果 34 第二節 隨機森林 38 第三節 電網系統個案分析 42 第四節 電網系統區域間比較 64 第五章 結論與建議 67 第一節 結論 67 第二節 建議 69 參考文獻 70zh_TW
dc.format.extent 6289217 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111257001en_US
dc.subject (關鍵詞) 電網脆弱度zh_TW
dc.subject (關鍵詞) 永續發展目標zh_TW
dc.subject (關鍵詞) 地理資訊系統zh_TW
dc.subject (關鍵詞) 隨機森林zh_TW
dc.subject (關鍵詞) Power Grid Vulnerabilityen_US
dc.subject (關鍵詞) Sustainable Development Goalsen_US
dc.subject (關鍵詞) Geographic Information Systemen_US
dc.subject (關鍵詞) Random Foresten_US
dc.title (題名) 以空間資訊技術建立電網脆弱度自然因素風險評估模型之研究zh_TW
dc.title (題名) Research on Establishing Power Grid Vulnerability Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中文參考文獻 何春蓀（1990）。普通地質學, 國立編譯館主編: 五南圖書出版有限公司。 余美儀（2017）。山區輸電鐵塔遇天然災害類型之危害分析探討［未出版之碩士論文］。國立雲林科技大學營建工程系。 吳秉昇、陳宇軒、王冠棋（2021）。雲林地層下陷之脆弱度評估與地理加權迴歸分析。中國地理學會會刊，（68），25-42。https://doi.org/10.29972/bgsc.202112_(68).0002 周桂田、許志義（2022）。我國電力供需問題及能源轉型策略。臺灣大學風險社會與政策研究中心。 梁啟源、劉致峻、鄭睿合、呂易恂、郭博堯、Liang, C.-y.、Liu, C.-c.、Jheng, R.-h.、Lu, Y.-h.、Kuo, P.-y.（我國能源脆弱度分析與因應策略建議。臺灣能源期刊，4卷卷4期期，頁361-400。 莊敏賢、何國謙、林俐玲（2006）。高壓電塔塔基安全評估之研究。坡地防災學報，5（2），1-14。https://doi.org/10.29995/jshr.200612.0001 許博鈞（2017）。應用台灣地震損失評估系統探討嘉義市歷史地震及鄰近活動斷層之地震災害與風險之研究［未出版之碩士論文］。國立成功大學地球科學系碩士在職專班。 陳柏榮、洪景山（2015）。臺灣電力公司閃電資料特徵分析。大氣科學，43（4），285-300。 陳磊、鄧欣怡、陳紅坤、石晶（2022）。電力系統韌性評估與提升研究綜述。電力系統保護與控制，50（13），11-22。 黃靜宜（2016）。棲蘭野生動物重要棲息環境之脆弱度評估。台灣生物多樣性研究，18（1），93-108。 趙文衡（2018）。國際電網韌性發展趨勢對我國能源合作的啟示。107年度強化APEC參與及雙邊與多邊能源國際合作之推動與研析。台灣經濟研究院 鄭國鑫, 雷., 王湘, 羅小春（2020）。地震災害模擬及配電網的風險評估。電工技術學報，35（24），5218-5226。https://doi.org/10.19595/j.cnki.1000-6753.tces.191495 魏震波、劉俊勇、朱國俊、朱康、劉友波、王民昆（2010）。基於可靠性加權拓撲模型下的電網脆弱性評估模型。電工技術學報，25（8），131-137。   英文參考文獻 Abedi, A., Gaudard, L., & Romerio, F. 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