Analysis of wind energy potential and wind energy development to evaluate performance of wind turbine installation in Bali, Indonesia

Authors

  • Nyoman Ade Satwika Institut Teknologi Sepuluh Nopember Surabaya
  • R. Hantoro Department of Engineering Physics, Institut Teknologi Sepuluh Nopember 60111 Surabaya, East Java, Indonesia, Phone: +6281337188263
  • E. Septyaningrum Department of Engineering Physics, Institut Teknologi Sepuluh Nopember 60111 Surabaya, East Java, Indonesia, Phone: +6281337188263
  • A. W. Mahmashani Department of Engineering Physics, Institut Teknologi Sepuluh Nopember 60111 Surabaya, East Java, Indonesia, Phone: +6281337188263

DOI:

https://doi.org/10.15282/jmes.13.1.2019.09.0379

Keywords:

Energy sustainability, Wind energy, wind turbine, wind average probability, wind power density

Abstract

In recent years, Wind power generation in Indonesia is no longer a new issue. Indonesia has average velocity from 2 m/s  to 7 m/s. With the characteristic it, Indonesia is suitable for small (10 kW) and medium wind turbine installation (10-100 kW. Based on the monitoring data from meteorological, climatological, and geophysical agency (BMKG), the average wind velocity in Bali is 2 m/s – 5m/s, hence Bali has potential to development and utilization the source for wind turbine installation, There are four stations of BMKG in Bali, which each station is supervise the region. Weibull distribution has been represented on this research to calculate and determine the probability of the each of region to know the availibility of the source. Literally,  Jembrana station has the lowest availability of power available from the district and cities in Bali, with 0-0.2 W/m2, compared with some districts and cities in Bali, with wind density power between 0-2.88 W/m2 and also the KHK station has the highest probabiity of wind velocity than the other regions. Reconstruction design had been done, with basic data from probability in Bali. The result shows that the redesign of wind turbine give an effective power to extract the wind source.

References

Sah BP and Wijayatunga P. GIS-based decision support system for renewable energy development: an Indonesian case study. Sustainable Development Conference, Manila, Philippines. 2017.

Hardianto T, Supeno B, Saleh A, Setiawan DK, Gunawan, Indra S. Potential of wind energy and design configuration of wind farm on puger beach at Jember Indonesia. World Engineering Summit – Applied Energy Symposium. Forum Low Carbon Cities Urban Energy Conference 2017;143:579-584.

Qing X. Statistical analysis of wind energy characteristics in Santiago island, Cape Verde, Journal of Renewanle Energy 2018;115:448-461.

Liu J, A statistical analysis of wind power density based on the weibull models for Fujian province in China. World Non-Grid Connected Wind Power and Energy Conference, Nanjing, China. 2009.

Hossieni A, Rasouli V, and Rasouli S. Wind energy potential assessment in order to produce electrical energy for case study in Divandareh, Iran. 3rd International Conference on Renewable Energy Research and Applications, Milwakuee, USA. 2014.

Kondapani P, Sakri SG. Preliminary study to assess the wind energy potential in Gulbarga, Karnataka state. Technology Conference, Hyderabad, India. 2008

Kumaraswarmy BG, Keshavan BK, Ravikiran YT. Analysis of seasonal wind speed and wind power density distribution in Aimangala wind form at Chitradurga Karnataka using two parameter weibull distribution. Power and Energy Society General Meeting, Detroit, MI, USA. 2011;03:3-6.

Ted S, Hatem A, Ayman AQ, Bert B, Ajerken D, Marius P, Pragasen P. Urban wind energy : Some views on potential and challenges, Journal of Wind Engineering and Industrial Aerodynamics 2018;179:146-157.

Dupont E, Koppelaar R, Jeanmart H. Global available wind energy with physical and energy return on investment constraints. Journal of Applied Energy 2017;209:322-338.

Ramirez FJ, Escribano AH, Lazaro EG, Pham DT. The role of wind energy production in addressing the European renewable energy targets : The case of Spain. Journal of Cleaner Production 2018;196:1198-1212.

Seyit AA, Onder G. Alternative moment method for wind energy potential and turbine energy output estimation. Journal of Renewable Energy 2018;120:69-77.

Jin Y, Ju P, Rehtanz C, Wu F, Pan X. Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm. Journal of Applied Energy 2018;222:485-496.

Adam H, Norman MF, and Aiguo D. The probability distribution of land surface wind speeds. Journal of American Meteorological Society 2011;24:3892-3909.

Naima J, Mohammed R, Benaissa ELF. Three-dimensional modeling of a horizontal axis wind turbine blade and profile effect analysis. International Renewable and Sustaiability Energy Conference, Marrakech, Morocco. 2016.

Ahmed AS. Wind energy characteristics and wind park installation in Shark El-Ouinat , Egypt. Journal of Renewable and Sustainable Energy 2018;82:734-742.

Bottasso CL, A. Croce, F. Gualdoni, and P. Montinari, A new concept to mitigate loads for wind turbines based on a passive flap. Proceeding of American Control Conference. 2015.

Akdağ SA, Güler O. Alternative moment method for wind energy potential and turbine energy output estimation. Journal of Renewable Energy 2018;120:69-77.

Sia CV, Fernando L, Joseph A, Chua SN. Modified Weibull analysis on banana fiber strength prediction. Journal of Mechanical and Engineering Sciences 2018;12:3461-3471.

Honrubia A. The influence of wind shear in wind turbine power estimation,in UPCT Conference, 2010.

Kaplan YA. Overview of wind energy in the world and assessment of current wind energy policies in Turkey. Journal of Renewable and Sustainable Energy 2015;43:562-568.

Quetzalcoatl HE, Alberto JPM, Francisco MA. Wind energy research in Mexico. Journal of Renewable energy 2018;123:719-729.

Kantar YM, Usta I. Analysis of the upper-truncated weibull distribution for wind speed. Journal of Energy Conversion and Management 2015;96:81-88.

Almalki SJ, Nadarajah S. Modifications of the weibull distribution : A review. Journal ,of Reliabillity Engineering and System Safety 2014;124:32-55.

Aririguzo JC, Ekwe EB. Manufacturing weibull distribution analysis of wind energy prospect for Umudike, Nigeria for power generation, Journal of Robot. and Computer - Integration Manufacturing 2018;55:160-163.

Wais P. Two and three-parameter weibull distribution in available wind power analysis. Journal of Renewable Energy 2017;(103):15-29.

Emre KOC, Onur G, Tahir Y. Comparison of qblade and cfd results for small-Scaled horizontal axis wind turbine analysis. International Conference on Renewable Energy Research and Applications, Brimingham.2016.

Supeni EE, Epaarachchi JA, Islam MM, Lau KT. Development of artificial neural network model in predicting performance of the smart wind turbine blade. Journal of Mechanical Engineering and Sciences 2014;6:734-745.

Munot MA, Yassin A, Shazali STS. Analysis of Production planning activities in remanufacturing system. Journal of Mechanical Engineering and Sciences 2018;12:3548-3565.

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Published

2019-03-28

How to Cite

[1]
N. A. Satwika, R. Hantoro, E. Septyaningrum, and A. W. Mahmashani, “Analysis of wind energy potential and wind energy development to evaluate performance of wind turbine installation in Bali, Indonesia”, J. Mech. Eng. Sci., vol. 13, no. 1, pp. 4461–4476, Mar. 2019.