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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
Liou, Wei-Jie
Keywords: 離岸風力發電
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。
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.


4C offshore,
Asia Wind Energy Association,
British Petroleum,
Siemens Gamesa,
Siemens Gamesa,
The European Wind Energy Association,
Global Wind Energy Council,
University of Virginia,
Description: 碩士
Source URI:
Data Type: thesis
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