Determination of Optimal Time-Average Wind Speed Data in the Southern Part of Malaysia

Main Article Content

Daniel Derome
https://orcid.org/0000-0003-2051-8133
Halim Razali
Ahmad Fazlizan
https://orcid.org/0000-0002-1614-3327
Alias Jedi
Katie Purvis- Roberts
https://orcid.org/0000-0003-1723-5968

Abstract

Mersing is one of the places that have the potential for wind power development in Malaysia. Researchers often suggest it as an ideal place for generating electricity from wind power. However, before a location is chosen, several factors need to be considered. By analyzing the location ahead of time, resource waste can be avoided and maximum profitability to various parties can be realized. For this study, the focus is to identify the distribution of the wind speed of Mersing and to determine the optimal average of wind speed. This study is critical because the wind speed data for any region has its distribution. It changes daily and by season. Moreover, no determination has been made regarding selecting the average wind speed used for wind studies. The wind speed data is averaged to 1, 10, 30, and 60 minutes and used to find the optimal wind speed average. This study used Kolmogorov-Smirnov and Chi-Square as the goodness of fit. The finding shows that the wind speed distribution in Mersing varies according to the time average used and the best fit distribution is Gen. Gamma.  In contrast, the optimal average wind speed is 10 minutes due to the highest similarity results with 1-minute data. These affect the reliability of the finding, accuracy of the estimation and decisions made. Therefore, the implementation of this study is significant so that the wind distribution in a particular area is more accurate.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Derome D, Razali H, Fazlizan A, Jedi A, Roberts KP-. Determination of Optimal Time-Average Wind Speed Data in the Southern Part of Malaysia. Baghdad Sci.J [Internet]. 2022 Oct. 1 [cited 2022 Nov. 30];19(5):1111. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6472
Section
article

References

Ibrahim MZ, Yong KH, Ismail M, Albani A, Muzathik AM. Wind characteristics and GIS-based spatial wind mapping study in Malaysia. J Sust Sci Manag. 2014; 9(2): 1-20.

Belhamadia A, Mansor M, Younis MA. A study on wind and solar energy potentials in Malaysia. Int J Renew Energy Res. 2014; 4(4): 1042-1048. doi:10.1109/CEAT.2013.6775617

Azad K, Rasul M, Halder P, Sutariya J. Assessment of wind energy prospect by Weibull distribution for prospective wind sites in Australia. Energy Procedia. 2019; 160(2018): 348-355. doi:10.1016/j.egypro.2019.02.167

Beabpimai W, Chitsomboon T. Numerical study of effect of blade twist modifications on the aerodynamic performance of wind turbine. Int J Renew En Dev. 2019; 8(3): 285-292. doi:10.14710/ijred.8.3.285-292

Daoudi M, Abdelaziz ASM, Mohammed E, Elmostapha E. Wind speed data and wind energy potential using Weibull distribution in Zagora, Morocco. Int J Renew En Dev. 2019; 8(3): 267-273. doi:10.14710/ijred.8.3.267-273

Zaharim A, Najid SK, Razali AM, Sopian K. The Suitability of Statistical Distribution in Fitting Wind Speed Data. WSEAS Trans Math. 2008; 7(12): 386-389.

Albani A, Ibrahim M, Yong K. Wind Energy Investigation in Northern Part of Kudat, Malaysia. Int J Appl Sci Eng. 2013; 2(2): 14-22. http://www.academia.edu/download/30886418/Technology_Engineering_2.pdf.

IRENA. Data and Statistics - IRENA REsource (Capacity and Generation). International Renewable Energy Agency. 2017. http://resourceirena.irena.org/gateway/dashboard/?topic=4&subTopic=16. .

Didane DH, Ab Wahab A, Shamsudin SS, Rosly N. Wind as a sustainable alternative energy source in Malaysia - a review. ARPN J Eng Appl Sci. 2016; 11(10): 6442-6449.

Mohsen A-ZA, Al-Jiboori MH, Al-Timimi YK. Investigating the Aerodynamic Surface Roughness Length over Baghdad City Utilizing Remote Sensing and GIS Techniques. Baghdad Sci J. 2021; 18(2(Suppl.)): 1048. doi:10.21123/bsj.2021.18.2(suppl.).1048

Sang LQ, Maeda T, Kamada Y. Study effect of extreme wind direction change on 3-bladed horizontal axis wind turbine. Int J Renew En Dev. 2019; 8(3): 261-266. doi:10.14710/ijred.8.3.261-266

Pobočíková I, Sedliačková Z, Michalková M. Application of Four Probability Distributions for Wind Speed Modeling. Procedia Eng. 2017; 192: 713-718. doi:10.1016/j.proeng.2017.06.123

Jung C, Schindler D. Global comparison of the goodness-of-fit of wind speed distributions. Energy Convers Manag. 2017; 133: 216-234. doi:10.1016/j.enconman.2016.12.006

Sohoni V, Gupta S, Nema R. A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turk J Elec Eng Comp Sci. 2016; 24(6): 4724-4735. doi:10.3906/elk-1412-207

Wais P. A review of Weibull functions in wind sector. Renew Sustain Energy Rev. 2017; 70(February 2015): 1099-1107. doi:10.1016/j.rser.2016.12.014

Ozay C, Celiktas MS. Statistical analysis of wind speed using two-parameter Weibull distribution in AlaçatI region. Energy Convers Manag. 2016; 121: 49-54. doi:10.1016/j.enconman.2016.05.026

Chaurasiya PK, Ahmed S, Warudkar V. Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques. Renew Energy. 2018; 115: 1153-1165. doi:10.1016/j.renene.2017.08.014

Wang J, Li Y. Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy. Appl Energy. 2018; 230(April): 429-443. doi:10.1016/j.apenergy.2018.08.114

Huang Y, Liu S, Yang L. Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM. Sustainability. 2018; 10(10): 3693. doi:10.3390/su10103693

Daut I, Razliana ARN, Irwan YM, Farhana Z. A study on the wind as renewable energy in Perlis, northern Malaysia. Energy Procedia. 2012; 18: 1428-1433. doi:10.1016/j.egypro.2012.05.159

Rasham AM. Analysis of Wind Speed Data and Annual Energy Potential at Three locations in Iraq. Int J Comput Appl. 2016; 137(11): 5-16. doi:10.5120/ijca2016908862

Murthy KSR, Rahi OP. A comprehensive review of wind resource assessment. Renew. Sust. Energ. Rev. 2017; 72(May 2015): 1320-1342. doi:10.1016/j.rser.2016.10.038

Derome D, Razali H, Fazlizan A, Mat S, Elias MA. Wind Speed Distribution: A case study of Mersing, Malaysia. In: Proceeding of 2nd Malaysia University-Industry Green Building Collaboration. ; 2018: 269.

Masseran N, Razali AM, Ibrahim K, Zin WZW, Zaharim A. On spatial analysis of wind energy potential in Malaysia. WSEAS Trans Math. 2012; 11(6): 467-477.

Sopian K, Othman MY, Wirsat A. The wind energy potential of Malaysia. Renew Energy. 1995; 6(8): 1005-1016. doi:10.1016/0960-1481(95)00004-8

Sanusi N, Zaharim A, Mat S. Wind energy potential: A case study of Mersing, Malaysia. ARPN J Eng Appl Sci. 2016; 11(12): 7712-7716.

Nortazi S. Analisis potensi tenaga angin di Malaysia: Suatu kajian terhadap taburan kebarangkalian angin. Doctoral dissertation, Universiti Kebangsaan Malaysia. 2019. doi: http://ptsldigital.ukm.my:8080/vital/access/manager/Repository/ukmvital:120934.

Chaurasiya PK, Ahmed S, Warudkar V. Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument. Alex Eng J. 2017.

Siddiqui A, Khan J, Ahmed F, Uddin Z, Iqbal S, Jilani S, et al. Determination of Weibull Parameter by Four Numerical Methods and Prediction of Wind Speed in Jiwani (Balochistan). J Basic Appl Sci. 2015; 11: 62-68. doi:10.6000/1927-5129.2015.11.08

Masseran N, Razali AM, Ibrahim K, Zaharim A, Sopian K. The Probability Distribution Model of Wind Speed over East Malaysia. Res J Appl Sci Eng Tech. 2013; 6(10): 1774-1779.

Aries N, Boudia SM, Ounis H. Deep assessment of wind speed distribution models: A case study of four sites in Algeria. Energy Convers Manag. 2018; 155(August 2017): 78-90. doi:10.1016/j.enconman.2017.10.082

Bhattacharya P, Bhattacharjee R. A Study on Weibull Distribution for Estimating the Parameters. J Appl Quant Methods. 2010; 33(5): 469-476. doi:10.1260/030952409790291163

Azad AK, Rasul MG, Yusaf T. Statistical diagnosis of the best Weibull methods for wind power assessment for agricultural applications. Energies. 2014; 7(5): 3056-3085. doi:10.3390/en7053056

Cetinay H, Kuipers FA, Guven AN. Optimal siting and sizing of wind farms. Renew Energy. 2017; 101: 51-58. doi:10.1016/j.renene.2016.08.008

Saberi Z, Fudholi A, Sopian K. Fitting of Weibull distribution method to analysis wind energy potential at Kuala Terengganu, Malaysia. J Adv Res Fluid Mech Therm Sci. 2020; 66(1): 1-11.

Marih S, Ghomri L, Bekkouche B. Evaluation of the wind potential and optimal design of a wind farm in the arzew industrial zone in Western Algeria. Int J Renew En Dev. 2020; 9(2): 177-187. doi:10.14710/ijred.9.2.177-187

Ouarda T B M J, Charron C, Shin J Y, Marpu P R, Al-Mandoos A H, Al-Tamimi M H, et al. Probability distributions of wind speed in the UAE. Energy Convers Manag. 2015; 93: 414-434. doi:10.1016/j.enconman.2015.01.036

Wais P. Two and three-parameter Weibull distribution in available wind power analysis. Renew Energy. 2016; 103: 15-29. doi:10.1016/j.renene.2016. 10.041