Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/137047
|
| Title: | 台灣西部海域離岸風力發電選址自然災害風險評估之研究
The study of natural disaster risk assessment for offshore wind power generation site in the western coasts of Taiwan |
| Authors: | 劉維傑
Liou, Wei-Jie |
| Contributors: | 甯方璽
Ning, Fang-Shii
劉維傑
Liou, Wei-Jie |
| Keywords: | 離岸風力發電
空間資訊
風險評估
模糊層級分析法
Offshore wind turbine
Spatial information
Risk assessment
Fuzzy Analytic Hierarchy Process |
| Date: | 2021 |
| Issue Date: | 2021-09-02 17:37:01 (UTC+8) |
| Abstract: | 隨著經濟不斷發展，近幾年來受到全球暖化的影響日益加劇，對此環保意識逐漸抬頭，而聯合國永續發展目標亦有多項指標對於環境保護有所規範。考量到國外再生能源發展之技術成熟性及台灣本島地狹人稠等諸多特性，離岸風力發電為永續能源之最佳選擇。
政府政策亦朝前揭目標邁進，全力推動離岸風力發電。目前台灣離岸風場之選址主要以風能密度作為考量，對於該場址自然災害所造成的風險較無著墨。為此，本研究將以空間資訊之技術，結合美國航空暨太空總署現代研究應用回顧分析之風力資料、中央氣象局-臺灣海域各地浪高週期波向觀測月統計資料、台灣地震危害度分析之475年回歸期的機率式地震危害度分析資料、台灣電力公司落雷監測歷史資料，首先對於台灣周圍地區之自然災害，利用專家問卷，以模糊層級分析法解算出離岸風力發電機各部件所受到自然災害之影響程度，綜整上述數據，最後建立起台灣西部海域離岸風力發電自然災害選址風險評估模型，以供後續選址規劃之使用。
本研究之成果，各零件機組受自然災害影響比重，發電機、轉子葉片、轉子機頭皆受到風力影響的程度最高；塔受到地震的影響程度最高；變壓器受到雷擊的影響程度最高。而就風機整體而言，風力被評定為最具威脅之自然災害，其次為雷擊、海浪、地震。
本研究成果針對各項自然災害空間分布進一步解釋，並對風機各零件機組所受自然災害影響程度進行分析，最後得出自然災害選址風險評估模型，並以標準化的指標(1~10)區分風機於該地區的自然災害風險程度，藉由該指標，本研究得以找出最適建立離岸風機之場址。
With the long-lasting development of the economy, the impact of global warming has been increasing in recent years. Therefore, the environmental protection awareness has gradually aroused. The United Nations Sustainable Development Goals (SDGs) also release several criterions to promote environmental protection. Consider Taiwan’s small land area and high population density, offshore wind power is the wise choice for sustainable energy in Taiwan.
At present, the site selection of Taiwan`s offshore wind farm is mainly based on wind energy density but not take the risks caused by natural disasters into account. Therefore, this research will utilize space information technology and natural disaster big data, through expert questionnaires based on Fuzzy Analytic Hierarchy Process(FAHP), obtain the weight of single natural disasters impact the components of offshore wind turbine, and finally established the natural disaster risk assessment model for offshore wind turbine in the western coast of Taiwan.
Based on the results of FAHP, the weight of single component impacted by natural disasters, wind is the most threatening disaster to generators, rotor blades, and rotor hub. Earthquakes is the most threatening disaster to towers. Thunder is the most threatening disaster to transformers. For wind turbines, wind is the most threatening natural disaster, followed by thunder, sea waves, and earthquakes.
This research briefly explains the spatial distribution of various natural disasters and analyze the weight of natural disasters affected by the component of wind turbine. The natural disaster risk assessment model for offshore wind farm is based on standardized indicators (1~10) to distinguish the risk index of each wind turbine. With this index, this model can find the most suitable site for offshore wind turbines. |
| Reference: | Andrawus, J. A. (2008). Maintenance optimisation for wind turbines (Doctoral dissertation).
Al-Yahyai, S., Charabi, Y., Gastli, A., & Al-Badi, A. (2012). Wind farm land suitability indexing using multi-criteria analysis. Renewable Energy, 44, 80-87.
Astudillo, L., Castillo, O., Melin, P., Alanis, A., Soria, J., & Aguilar, L. T. (2006). Intelligent Control of an Autonomous Mobile Robot using Type-2 Fuzzy Logic. Engineering Letters, 13(3).
Aydin, N. Y., Kentel, E., & Duzgun, S. (2010). GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey. Renewable and Sustainable Energy Reviews, 14(1), 364-373.
Besnard, F. (2013). On maintenance optimization for offshore wind farms. Chalmers University of Technology.
Bevilacqua, M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering & System Safety, 70(1), 71-83.
Blackshaw, P. (2008). Satisfied customers tell three friends, angry customers tell 3,000: running a business in today`s consumer-driven world. Currency.
Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach`s alpha. Bmj, 314(7080), 572.
Bodansky, D. (1993). The United Nations framework convention on climate change: a commentary. Yale J. Int`l l., 18, 451.
Bravo, J. D., Casals, X. G., & Pascua, I. P. (2007). GIS approach to the definition of capacity and generation ceilings of renewable energy technologies. Energy Policy, 35(10), 4879-4892.
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
Carrión, J. A., Estrella, A. E., Dols, F. A., Toro, M. Z., Rodríguez, M., & Ridao, A. R. (2008). Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renewable and sustainable energy reviews, 12(9), 2358-2380.
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
Chen, F., Lu, S. M., Tseng, K. T., Lee, S. C., & Wang, E. (2010). Assessment of renewable energy reserves in Taiwan. Renewable and Sustainable Energy Reviews, 14(9), 2511-2528.
Charabi, Y., & Gastli, A. (2011). PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renewable Energy, 36(9), 2554-2561.
Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International journal of market research, 50(1), 61-104.
DeVellis, R. F. (2016). Scale development: Theory and applications (Vol. 26). Sage publications.
DNV, G. (2014). DNV-OS-J101–Design of offshore wind turbine structures. DNV GL: Oslo, Norway.
Florian, M., & Sørensen, J. D. (2017). Risk-based planning of operation and maintenance for offshore wind farms. Energy Procedia, 137, 261-272.
Forman, E. H., Saaty, T. L., Selly, M. A., & Waldron, R. (1983). Expert choice. Decision Support Software. McLean, VA.
Franek, J., & Kresta, A. (2014). Judgment scales and consistency measure in AHP. Procedia Economics and Finance, 12, 164-173.
George, D., & Mallery, M. (2003). Using SPSS for Windows step by step: a simple guide and reference.
Goguen, J. A. (1967). L-fuzzy sets. Journal of mathematical analysis and applications, 18(1), 145-174.
Kang, H. Y., Hung, M. C., Pearn, W. L., Lee, A. H., & Kang, M. S. (2011). An integrated multi-criteria decision making model for evaluating wind farm performance. Energies, 4(11), 2002-2026.
Karyotakis, A. (2011). On the optimisation of operation and maintenance strategies for offshore wind farms (Doctoral dissertation, UCL (University College London)).
Kaya, T., & Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, 35(6), 2517-2527.
Kolios, A., Collu, M., Chahardehi, A., Brennan, F. P., & Patel, M. H. (2010, April). A multi-criteria decision making method to compare support structures for offshore wind turbines. In European Wind Energy Conference, Warsaw.
Lee, A. H., Chen, H. H., & Kang, H. Y. (2009). Multi-criteria decision making on strategic selection of wind farms. Renewable Energy, 34(1), 120-126.
Lee, A. H., Hung, M. C., Kang, H. Y., & Pearn, W. L. (2012). A wind turbine evaluation model under a multi-criteria decision making environment. Energy Conversion and Management, 64, 289-300.
Lee, A. H., Hung, M. C., Pearn, W. L., & Kang, H. Y. (2014). An analytical model for evaluating wind turbine types. In Applied Mechanics and Materials (Vol. 543, pp. 333-336). Trans Tech Publications Ltd.
Leung, D. Y., & Yang, Y. (2012). Wind energy development and its environmental impact: A review. Renewable and Sustainable Energy Reviews, 16(1), 1031-1039.
Li, Q., Ma, Y., Guo, Z., Ren, H., Wang, G., Arif, W., ... & Siew, W. H. (2017). The lightning striking probability for offshore wind turbine blade with salt fog contamination. Journal of Applied Physics, 122(7), 073301.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Lin, Z. C., & Yang, C. B. (1996). Evaluation of machine selection by the AHP method. Journal of Materials Processing Technology, 57(3-4), 253-258.
Machmood, K., & Shevtshenko, E. (2015). Analysis of machine production processes by risk assessment approach. Journal of Machine Engineering, 15.
Maity, S. R., & Chakraborty, S. (2012). Turbine blade material selection using fuzzy analytic network process. International Journal of Materials and Structural Integrity, 6(2-4), 169-189.
Malczewski, J. (1999). GIS and multicriteria decision analysis. John Wiley & Sons.
Melin, P., Urias, J., Solano, D., Soto, M., Lopez, M., & Castillo, O. (2006). Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms. Engineering Letters, 13(3).
Miller, G. A. (1951). Language and communication.
Mohsen, T., & Reg, D. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2(1), 53-55.
Mulargia, F., Stark, P. B., & Geller, R. J. (2017). Why is probabilistic seismic hazard analysis (PSHA) still used?. Physics of the Earth and Planetary Interiors, 264, 63-75.
Nadaï, A. (2007). “Planning”,“siting” and the local acceptance of wind power: Some lessons from the French case. Energy policy, 35(5), 2715-2726.
Nijhuis, J. A. G., Ter Brugge, M. H., Helmholt, K. A., Pluim, J. P. W., Spaanenburg, L., Venema, R. S., & Westenberg, M. A. (1995, December). Car license plate recognition with neural networks and fuzzy logic. In Proceedings of ICNN`95-International Conference on Neural Networks (Vol. 5, pp. 2232-2236). IEEE.
Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-hill education.
Outlook, B. E. (2019). 2019 edition. London, United Kingdom2019.
Patnala, P. K., Parida, M., & Chalumuri, R. S. (2020). A decision framework for defining Transit-Oriented Development in an indian city. Asian Transport Studies, 6, 100021.
Protocol, K. (1997). United Nations framework convention on climate change. Kyoto Protocol, Kyoto, 19, 497.
Protocol, M. (1987). Montreal protocol on substances that deplete the ozone layer. Washington, DC: US Government Printing Office, 26, 128-136.
Rew, L. (1988). Intuition in decision‐making. Image: The Journal of Nursing Scholarship, 20(3), 150-154.
Robert, K. W., Parris, T. M., & Leiserowitz, A. A. (2005). What is sustainable development? Goals, indicators, values, and practice. Environment: science and policy for sustainable development, 47(3), 8-21.
Saaty, T. L. (1988). What is the analytic hierarchy process?. In Mathematical models for decision support (pp. 109-121). Springer, Berlin, Heidelberg.
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
Sánchez-Lozano, J. M., Antunes, C. H., García-Cascales, M. S., & Dias, L. C. (2014). GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renewable Energy, 66, 478-494.
Sánchez-Lozano, J. M., García-Cascales, M. S., Lamata, M. T., & Sierra, C. (2014). Decision criteria for optimal location of wind farms. In Exploring Innovative and Successful Applications of Soft Computing (pp. 199-215). IGI Global.
Sánchez-Lozano, J. M., García-Cascales, M. S., & Lamata, M. T. (2016). Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. Journal of cleaner production, 127, 387-398.
San Cristóbal, J. R. (2011). Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method. Renewable energy, 36(2), 498-502.
Shafiee, M. (2015). A fuzzy analytic network process model to mitigate the risks associated with offshore wind farms. Expert Systems with Applications, 42(4), 2143-2152.
Shafiee, M., & Dinmohammadi, F. (2014). An FMEA-based risk assessment approach for wind turbine systems: a comparative study of onshore and offshore. Energies, 7(2), 619-642.
Shafiee, M., Patriksson, M., Strömberg, A. B., & Bertling, L. (2013, June). A redundancy optimization model applied to offshore wind turbine power converters. In 2013 IEEE Grenoble Conference (pp. 1-6). IEEE.
Suliman, A. A. A. D. A., Ali, A. O. H., & Hamid, B. A. H. A. (2017). Study of Induction Generator In Wind Energy (Doctoral dissertation, Sudan University of Science and Technology).
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach`s alpha. International journal of medical education, 2, 53.
Tegou, L. I., Polatidis, H., & Haralambopoulos, D. A. (2010). Environmental management framework for wind farm siting: Methodology and case study. Journal of environmental management, 91(11), 2134-2147.
Triantaphyllou, E., Kovalerchuk, B., Mann, L., & Knapp, G. M. (1997). Determining the most important criteria in maintenance decision making. Journal of Quality in Maintenance Engineering.
Tyagi, S. (2016). An improved fuzzy-AHP (IFAHP) approach to compare SECI modes. International Journal of Production Research, 54(15), 4520-4536.
Ulutaş, B. H. (2005). Determination of the appropriate energy policy for Turkey. Energy, 30(7), 1146-1161.
United Nations, World Meteorological Organization. (1992). Protecting the atmosphere, oceans and water resources: sustainable use of natural resources. Swiss Geneva: World Meteorological Organization.
Uyan, M. (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11-17.
Van de Kaa, G., van Ek, M., Kamp, L. M., & Rezaei, J. (2020). Wind turbine technology battles: Gearbox versus direct drive-opening up the black box of technology characteristics. Technological Forecasting and Social Change, 153, 119933.
Van Haaren, R., & Fthenakis, V. (2011). GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State. Renewable and sustainable energy reviews, 15(7), 3332-3340.
Wang, T. Y., & Chiang, H. M. (2007). Fuzzy support vector machine for multi-class text categorization. Information Processing & Management, 43(4), 914-929.
Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European journal of operational research, 186(2), 735-747.
Wolsink, M. (2010). Contested environmental policy infrastructure: Socio-political acceptance of renewable energy, water, and waste facilities. Environmental Impact Assessment Review, 30(5), 302-311.
Wolsink, M. (2010). Near-shore wind power—Protected seascapes, environmentalists’ attitudes, and the technocratic planning perspective. Land use policy, 27(2), 195-203.
Yeh, T. M., & Huang, Y. L. (2014). Factors in determining wind farm location: Integrating GQM, fuzzy DEMATEL, and ANP. Renewable Energy, 66, 159-169.
Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh (pp. 394-432).
Zhang, S. (2005, August). Problems experienced with operating wind farms in China. In 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific (pp. 1-5). IEEE.
Zhao, J., Wen, F., Dong, Z. Y., Xue, Y., & Wong, K. P. (2012). Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization. IEEE Transactions on industrial informatics, 8(4), 889-899.
Zidani, F., Benbouzid, M. E. H., Diallo, D., & Naït-Saïd, M. S. (2003). Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system. IEEE Transactions on energy conversion, 18(4), 469-475.
Zubaryeva, A., Zaccarelli, N., Del Giudice, C., & Zurlini, G. (2012). Spatially explicit assessment of local biomass availability for distributed biogas production via anaerobic co-digestion–Mediterranean case study. Renewable Energy, 39(1), 261-270.
王岩、袁璿、梁鵬程、肖瓊、馮學斌、侯彬彬，2019。風力發電系統防雷設計研究，收藏，35.
王昱凱、林俶寬、張欽森，2017。離岸風電面臨的挑戰與機會，中華技術，114，246-257.
呂學德、何無忌、呂威賢、胡哲魁、陳美蘭、連永順，2015。台灣離岸風力潛能與優選離岸區塊場址研究，電力工程研討會，36.
林俶寬、張上君、黃振愷、劉晉堯，2014。台灣地區離岸風場選址技術之探討，工業技術研究院，103(3)，62-71.
林榮貴，2011。我國既有大型風力機組故障原因探討分析，機械月刊，37(10)，62-72.
姬東朝、宋筆鋒、喻天翔，2007。基於模糊層次分析法的決策方法及其應用，火力與指揮控制，32(11)，38-41.
陳美蘭、胡哲魁，2014。台灣地區風能評估與離岸風電開發潛能分析，工業技術研究院，103(2)，38-49.
陳誠亮、王子奇、楊尚峰，2002。模糊理論簡介及其在家電與工業上的應用，國家實驗研究院儀科新知，24(1)，61-76.
康瀚文，2003。臺北縣市國民中學九年一貫課程教科書選用制度之研究，臺灣師範大學教育學系學位論文，1-271.
張吉軍，2000。模糊層次分析法，模糊系統與數學，14(2)，80-88.
褚志鵬，2009。層級分析法理論與實作，國立東華大學企業管理學系.
楊敏生，1994。模糊理論簡介，中央研究院數學傳播，18(1)，1-5.
鄭錦桐、林柏伸、江憲宗、李錫堤，2011。台灣的地震危害度分析，海峽兩岸地質災害研討會，57-73
龔景漢，2019。國外離岸風場引入保險之關鍵議題，國家發展研究，18(2)，153-187.
4C offshore，https://www.4coffshore.com/windfarms/windspeeds.aspx
Asia Wind Energy Association，https://www.asiawind.org/
Atkins，https://www.atkinsglobal.com/en-GB/angles/all-angles/floating-or-fixed
British Petroleum，https://www.bp.com/
EnergyTrend，https://www.energytrend.com.tw/knowledge/20110616-1866.html
Marketingdatascience，https://medium.com/marketingdatascience/%E6%9D%8E%E5%85%8B%E7%89%B9%E9%87%8F%E8%A1%A8-likert-scale-%E7%9A%84%E8%B5%B7%E6%BA%90%E5%92%8C%E4%BD%BF%E7%94%A8-8e1503ddd5db
NASA，https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/
Siemens Gamesa，https://pdf.archiexpo.com/pdf/siemens-gamesa/siemens-g4-platform-36-mw-40-mw-geared-wind-turbines/88089-189819.html
Siemens Gamesa，https://www.siemensgamesa.com/products-and-services/offshore/wind-turbine-swt-6-0-154
The European Wind Energy Association，http://www.ewea.org/Statistics/
Gemini，https://www.geminiwindpark.nl/details-tubines--nacelles.html
Global Wind Energy Council， https://gwec.net/global-offshore-wind-report-2020/
University of Virginia，https://data.library.virginia.edu/using-and-interpreting-cronbachs-alpha/
Wind-Turbine-Models，https://en.wind-turbine-models.com/turbines/601-siemens-swt-4.0-130
Wind-Turbine-Models，https://en.wind-turbine-models.com/turbines/657-siemens-swt-6.0-154
台灣電力公司，https://www.taipower.com.tw/tc/page.aspx?mid=96
台灣電力公司，https://www.taipower.com.tw/tc/page.aspx?cchk=b6134cc6-838c-4bb9-b77a-0b0094afd49d&cid=406&mid=206
全國法規資料庫，https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=A0000009
行政院，https://www.ey.gov.tw/Page/9277F759E41CCD91/6f0faa1c-9406-48d0-97aa-78ccea4f3f02
能源知識庫，https://km.twenergy.org.tw
財團法人中國驗船中心，https://www.crclass.org/chinese/content/publications/rules.html
氣象資料開放平台，https://opendata.cwb.gov.tw/dataset/climate/C-B0049-001
經濟部標準檢驗局，https://www.bsmi.gov.tw/bsmiGIP/wSite/ct?xItem=80293&ctNode=6598&mp=1
經濟部能源局，https://www.moeaboe.gov.tw/wesnq/
經濟部能源局，http://www.moeaboe.gov.tw/ECW/populace/Law/Content.aspx?menu_id=1850
經濟部能源局，http://www.moeaboe.gov.tw/ECW/populace/Law/Content.aspx?menu_id=2870
經濟部能源局，https://www.moeaboe.gov.tw/ECW/populace/content/ContentDesc.aspx?menu_id=5493
維基百科，https://zh.wikipedia.org/wiki/%E6%B5%B7%E6%B4%8B%E7%AB%B9%E5%8D%97%E9%A2%A8%E5%8A%9B%E7%99%BC%E9%9B%BB%E5%A0%B4
模糊理論，http://rportal.lib.ntnu.edu.tw/bitstream/20.500.12235/95760/2/n069475001302.pdf
層級分析法與模糊層級分析法理論與實證，https://slidesplayer.com/slide/11452228/ |
| Description: | 碩士
國立政治大學
地政學系
108257031 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108257031 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202101432 |
| Appears in Collections: | [地政學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 703101.pdf | | 5156Kb | Adobe PDF2 | 34 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
