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Analysis of risks that are based on the aerial photography used in photogrammetric monitoring maps for environmental wind power energy plant projects

  • Eray CanEmail author
Article
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Abstract

Currently, the role of energy is important to sustain and even improve the development and prosperity of a nation. Specifically, in the information age that we are living in with the increasing demands for ever-improving technology, the needs for this critical energy power grow each day. In sustainable electrical energy production, which has a substantial share among energy types, utilizing wind power instead of fossil fuels constitutes a prime role among alternative energy sources. To maintain these preferred wind energy power plant projects in more efficient and sustainable ways, their conformity with the relevant field is also very important. Field monitoring and map-producing studies could be performed with geodetic measurement methods with the aid of aerial photography methods. Once the aerial photography production method is implemented in a careful manner, ultimately, sensitive field and environmental monitoring maps can be obtained; however, some potential failures and risk factors could also be encountered during the production of these maps. In this research, potential failures and risk factors, relevant precautionary measures taken against these risks that could be encountered during the production of aerial photography monitoring maps (photogrammetry), and measurements that are used for producing environmental field models for wind power plant projects to be used for the production of electrical energy are studied with the FMEA (failure mode effect analysis) and the systematic Pareto analysis.

Keywords

Monitoring maps Aerial photographs Environmental wind energy power plants Failure mode effect analysis Pareto analysis Risk analysis 

Notes

References

  1. Ajayi, O. O. (2009). Assessment of utilization of wind energy resources in Nigeria. Energy Policy, 37, 750–753.  https://doi.org/10.1016/j.enpol.2008.10.020.CrossRefGoogle Scholar
  2. Akın, B. (1998). ISO 9000 failure mode and effect analysis (FMEA) Bilim Teknik Yayınevi, 182s, Istanbul (In Turkish)Google Scholar
  3. Amoo, O. M. (2012). Evaluation of the wind energy potential of two southwest sites in Nigeria. Frontiers in Energy, 6(3), 237–246.  https://doi.org/10.1007/s11708-012-0201-2.CrossRefGoogle Scholar
  4. Atalay, I. F., & Bekaroglu, N. (1973). Landslides and engineering application. Republic of Turkey General Directorate For Highways, Publication no: 200, p 349. Ankara, Turkey (in Turkish).Google Scholar
  5. Beccali, M., Cirrincione, G., Marvuglia, A., & Serporta, C. (2010). Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor. Applied Energy, 87, 884–893.  https://doi.org/10.1016/j.apenergy.2009.05.026.CrossRefGoogle Scholar
  6. Brouwer, S. R., Al-Jibouri, S. H. S., Cárdenas, I. C., & Halman, J. I. M. (2018). Towards analysing risks to public safety from wind turbines. Reliability Engineering and System Safety, 180, 77–87.  https://doi.org/10.1016/j.ress.2018.07.010.CrossRefGoogle Scholar
  7. Cellura, M., Cirrincione, G., Marvuglia, A., & Miraoui, A. (2008). Wind speed spatial estimation for energy planning in Sicily: a neural kriging application. Renewable Energy, 33, 1251–1266.  https://doi.org/10.1016/j.renene.2007.08.013.CrossRefGoogle Scholar
  8. Chin, K. S., Wang, Y. M., Poon Gary Ka, K., & Yang, J. B. (2009). Failure mode and effects analysis using a group-based evidential reasoning approach. Computers & Operations Research, 36(2009), 1768–1779.  https://doi.org/10.1016/j.cor.2008.05.002.CrossRefGoogle Scholar
  9. Coşkun, A. A., & Türker, Y. Ö. (2012). Wind energy and Turkey. Environmental Monitoring and Assessment, 184, 1265–1273.  https://doi.org/10.1007/s10661-011-2038-2.CrossRefGoogle Scholar
  10. Cristea, C., & Jocea, A. F. (2016). GIS Application for wind energy, Energy Procedia, 85(2016):132 – 140, Sustainable Solutions for Energy and Environment, EENVIRO - YRC 2015, 18-20 November 2015, Bucharest, Romania.  https://doi.org/10.1016/j.egypro.2015.12.283 CrossRefGoogle Scholar
  11. Ervural, B. C., Zaim, S., Demirel, O. F., Aydin, Z., & Delen, D. (2018). An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renewable and Sustainable Energy Reviews, 82, 1538–1550.  https://doi.org/10.1016/j.rser.2017.06.095.CrossRefGoogle Scholar
  12. Fang, J., Peringer, A., Stupariu, M. S., Patru-Stupariu, I., Buttler, A., Golay, F., & Porté-Agel, F. (2018). Shifts in wind energy potential following land-use driven vegetation dynamics in complex terrain. Science of the Total Environment, 639, 374–384.  https://doi.org/10.1016/j.scitotenv.2018.05.083.CrossRefGoogle Scholar
  13. Hughes, L., Jong, M. D., & Wang, X. Q. (2016). A generic method for analyzing the risks to energy systems. Applied Energy, 180, 895–908.  https://doi.org/10.1016/j.apenergy.2016.07.133.CrossRefGoogle Scholar
  14. Kang, J., Sun, L., Sun, H., & Wu, C. (2017). Risk assessment of floating offshore wind turbine based on correlation-FMEA. Ocean Engineering, 129, 382–388.  https://doi.org/10.1016/j.oceaneng.2016.11.048.CrossRefGoogle Scholar
  15. Kang, J., Sun, L., & Guedes Soares, C. (2019). Fault tree analysis of floating offshore wind turbines. Renewable Energy, 133, 1455–1467.  https://doi.org/10.1016/j.renene.2018.08.097.CrossRefGoogle Scholar
  16. Kucukali, S. (2016). Risk scorecard concept in wind energy projects: an integrated approach. Renewable and Sustainable Energy Reviews, 56, 975–987.  https://doi.org/10.1016/j.rser.2015.12.017.CrossRefGoogle Scholar
  17. Leimeister, M., & Kolios, A. (2018). A review of reliability-based methods for risk analysis and their application in the offshore wind industry. Renewable and Sustainable Energy Reviews, 91, 1065–1076.  https://doi.org/10.1016/j.rser.2018.04.004.CrossRefGoogle Scholar
  18. Li, J., & Yu, X. B. (2017). LIDAR technology for wind energy potential assessment: demonstration and validation at a site around Lake Erie. Energy Conversion and Management, 144, 252–261.  https://doi.org/10.1016/j.enconman.2017.04.061.CrossRefGoogle Scholar
  19. Liu, H. C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Systems with Applications, 40, 828–838.  https://doi.org/10.1016/j.eswa.2012.08.010.CrossRefGoogle Scholar
  20. Łopucki, R., Klich, D., & Gielarek, S. (2017). Do terrestrial animals avoid areas close to turbines in functioning wind farms in agricultural landscapes? Environmental Monitoring and Assessment, 189, 343.  https://doi.org/10.1007/s10661-017-6018-z.CrossRefGoogle Scholar
  21. Marugána, A. P., Márquez, F. P. G., Perez, J. M. P., & Ruiz-Hernández, D. (2018). A survey of artificial neural network in wind energy systems. Applied Energy, 228, 1822–1836.  https://doi.org/10.1016/j.apenergy.2018.07.084.CrossRefGoogle Scholar
  22. Miryousefi Aval, S. M., Ahadi, A., & Hayati, H. (2016). A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation. Frontiers in Energy, 10(1), 46–56.  https://doi.org/10.1007/s11708-015-0384-4.CrossRefGoogle Scholar
  23. Özcan, S. (2001). Pareto analysis one of the statistical process control technics and an application in the cement industry, Cumhuriyet University. Journal of Economics and Administrative Sciences, 2(2), 151–174 (In Turkish with English abstract).Google Scholar
  24. Rafiee, A., Van der Male, P., Dias, E., & Scholten, H. (2018). Interactive 3D geodesign tool for multidisciplinary wind turbine planning. Journal of Environmental Management, 205, 107–124.  https://doi.org/10.1016/j.jenvman.2017.09.042.CrossRefGoogle Scholar
  25. Ramachandra, T. V., & Shruthi, B. V. (2005). Wind energy potential mapping in Karnataka, India, using GIS. Energy Conversion and Management, 46, 1561–1578.  https://doi.org/10.1016/j.enconman.2004.07.009.CrossRefGoogle Scholar
  26. Rolik, Y. (2017). Risk management in implementing wind energy project. Procedia Engineering, 178, 278–288.  https://doi.org/10.1016/j.proeng.2017.01.115.CrossRefGoogle Scholar
  27. Salam, M. A., Yazdanı, M. G., Rahman, Q. M., Nurul, D., Meı, S. F., & Hasan, S. (2018). Investigation of wind energy potentials in Brunei Darussalam. Frontiers in Energy.  https://doi.org/10.1007/s11708-018-0528-4.
  28. Şen, Z. (1999). Terrain topography classification for wind energy generation. Renewable Energy, 16, 904–907.  https://doi.org/10.1016/S0960-1481(98)00304-8.CrossRefGoogle Scholar
  29. Sesli, F. A., Karsli, F., Colkesen, I., & Akyol, N. (2009). Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey. Environmental Monitoring and Assessment, 153, 391–403.  https://doi.org/10.1007/s10661-008-0366-7.CrossRefGoogle Scholar
  30. 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.  https://doi.org/10.3390/en7020619.CrossRefGoogle Scholar
  31. Shu, Z. R., Li, Q. S., He, Y. C., & Chan, P. W. (2016). Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications. Applied Energy, 169, 150–163.  https://doi.org/10.1016/j.apenergy.2016.01.135.CrossRefGoogle Scholar
  32. Su, X., Deng, Y., Mahadevan, S., & Bao, Q. (2012). An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades. Engineering Failure Analysis, 26, 164–174.  https://doi.org/10.1016/j.engfailanal.2012.07.009.CrossRefGoogle Scholar
  33. Tarawneh, Q. Y., & Şahin, A. D. (2003). Regional wind energy assessment technique with applications. Energy Conversion and Management, 44, 1563–1574.  https://doi.org/10.1016/S0196-8904(02)00164-4.CrossRefGoogle Scholar
  34. Tazi, N., Châtelet, E., & Bouzidi, Y. (2017). Using a hybrid cost-FMEA analysis for wind turbine reliability analysis. Energies, 10(3), 276.  https://doi.org/10.3390/en10030276.CrossRefGoogle Scholar
  35. Wang, Y. M., Chin, K. S., Poon Gary Ka, K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Systems with Applications, 36, 1195–1207.  https://doi.org/10.1016/j.eswa.2007.11.028.CrossRefGoogle Scholar
  36. Xiao, N., Huang, H. Z., Li, Y., He, L., & Jin, T. (2011). Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Engineering Failure Analysis, 18, 1162–1170.  https://doi.org/10.1016/j.engfailanal.2011.02.004.CrossRefGoogle Scholar
  37. Xinyao, J., Yongjun, H., & Fuchao, L. (2017). Research on the evaluation of wind power projects of investment risk. Procedia Computer Science, 111, 388–398.  https://doi.org/10.1016/j.procs.2017.06.039.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Yalova University, Engineering FacultyTransportation Engineering DepartmentYalovaTurkey

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