Application of Differential Evolution for Wind Turbine.

Shafiq-Ur-Rehman Massan; Asim Imdad Wagan; Muhammad Mujtaba Shaikh; Muhammad Saleh Shah

Author(s)

Shafiq-Ur-Rehman Massan Shaheed Zulfikar Ali Bhutto Institute of Science & Technology, Karachi, Pakistan

Asim Imdad Wagan Mohammad Ali Jinnah University, Karachi, Pakistan

Muhammad Mujtaba Shaikh Department of Basic Sciences & Related Studies, Mehran University of Engineering & Technology, Jamshoro, Pakistan

Muhammad Saleh Shah Government Saifee Zahabi Edhi College of Education, Karachi, Pakistan

Abstract

WTM (Wind Turbine Micrositing) has been an important topic of discussion in recent times. A number of Evolutionary Algorithms have been applied to the WTM problem. The DEA (Differential Evolution Algorithm) is used for a bi-constrained optimization for getting maximum power production at the least cost from a 2x2 km space. It is shown that the DEA performs comparably to the GA (GeneticAlgorithms) for wind farm optimization. The optimal configuration obtained enlists the number of turbines, the cost of power generated as well as the power produced. Moreover, this study is augmented by comparison with past approaches by using the GA for the same purpose.

Publication Details

Page(s) 353-366

DOI DOI not available

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 36, Issue: 2, Year: 2017

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References

Massan , S.-R.,Wagan , A.I.,Shaikh , M.M.,Abro, 2014.Wind Turbine Micrositing by Using the Firefly Algorithm”, -

Jensen, N.O., “A Note on Wind Generator Interaction”, RISO-M-2411, 1983.

Grady , S.A.,Hussaini,Abdullah , M.M.,RenewableEnergy, 2005.Placement of Wind Turbines using Genetic Algorithms”, 30 259 -270

Mittal , A., 2010.Optimization of the Layout of Large Wind Farms Using Genetic Algorithm”,MS Thesis -

Barthelmie , R.J.,Larsen , G.C.,Frandsen , S.T.,Folkerts , L.,Rados , K.,Pryor , S.C.,Lange , B.,Schepers, 2006.“Comparison of Wake Model Simulations with Offshore Wind Turbine Wake Profiles Measured by Sodar”,Journal of Atmospheric and Oceanic Technologies 23 888 -901

Chen , Y.,Li , H.,Jin , K.,Song , Q., 2013.Wind Farm Layout Optimization using Genetic Algorithm with Different Hub Height Wind Turbines”, 20 56 -65

Chowdhury , S.,Messac , A.,Castillo , L.,RenewableEnergy, .Unrestricted Wind Farm Layout Optimization (UWFLO): Investigating Key Factors Influencing the Maximum Power Generation”, 38 16 -30

do Couto,Farias , B.,Carlos , A.,Diniz , G.C.,Vinicius , M., .“Optimization of Wind Farm Layout Using Genetic Algorithm”,10th World Congress on Structural and Multidisciplinary Optimization 1 -10

Ituarte-Villarreal , C.M.,Espiritu , J.F., 2011.Optimization of Wind Turbine Placement Using a Viral Based Optimization Algorithm”,Procedia Computer Science 6 469 -474

Karampelas , P.,Ekonomou , L.,Fotis , G.P.,Vita, .Evaluation of the Optimal Number of Wind Turbines in a Wind Farm Using the Downhill Simplex Optimization Method”,International Journal on Power System Optimization 3 11 -14

Kusiak , A.,Song , Z.,RenewableEnergy, 2010.Design of Wind Farm Layout for Maximum Wind Energy Capture”, 35 685 -694

Larsen , G.C., .A Simple Wake Calculation Procedure”, 2760 -

Storn , R.,Price , K.,“Differential Evolution - A Simple, 1997.Efficient Heuristic for Global Optimization Over Continuous Spaces”,Journal of Global Optimization, No. 11 341 -359

Rajper , S.,Amin , I.J., .Optimization of Wind Turbine Micrositing: A Comparative Study”, Renewable and Sustainable Energy Reviews, 16 5485 -5492

Mosetti , G.,Poloni , C.,Diviacco , B., 1994.Optimization of Wind Farms Positioning in Large Wind Farms by Means of a Genetic Algorithm”, 51 105 -116

Marmidis , G.,Lazarou , S.,Pyrgioti,RenewableEnergy, 2008.Optimal Placement of Wind Turbines in a Wind Park Using Monte Carlo Simulation”,No. 33 1455 -1460

Emami , A.,Nougreh , P., 2010.New Approach on Optimization in Placement of Wind Turbines within Wind Farm by Genetic Algorithms”,No. 35 169 -178

Jensen , N.O.,RISO-M-, 1983.“A Note on Wind Generator Interaction”, -

Lee , J. B., 2011.,[20] [21] [22] [23] [24] [25] -

Lee, J. B., “Clever Algorithms - Nature Inspired Programming Recipes”, Melbourne, Jason Brown Lee, 2011.

Hegerty, B., Hung, C.-C., and Kasprak, K., “A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems”, Southern Polytechnic State University, Marietta, GA

Salcedo-Sanz, S., Xu, Y., and Yao, X., “Hybrid Meta-Heuristics Algorithms for Task Assignment in Heterogeneous Computing Systems”, Computers and Operations Research, Volume 33, No. 3, pp. 820-835,2006.

Codreanu, I., “Parallel between Differential Evolution and Genetic Algorithms with Exemplification in a Microfluidics Optimization Problem”, Proceedings of International Conference on Semiconductor, pp. 421-424, 2005.

Sentinella, M.R., “Comparison and Integrated use of Differential Evolution and Genetic Algorithms for Space Trajectory Optimization”, IEEE Congress on Evolutionary Computation, pp. 973-978, 2007.

Mezura-Montes, E., “Nature-Inspired Algorithms Evolutionary and Swarm Intelligence Approaches” A Tutorial in MICAI, 2008.

Teo, J., “Exploring Dynamic Self-Adaptive Populations in Differential Evolution”, Soft Computing, No. 10, pp. 673-686, 2006

Massan, S.R., Wagan, A.I., and Shaikh, M.M., “Power Optimization of Wind Turbines by the Adjoint Method”, Sindh University Research Journal (Science Series), Volume 48, No. 3, pp. 559-562, Jamshoro, Pakistan, 2016.