Wind farm layout optimization using imperialist competitive algorithm

In this work, the wind farm layout optimization problem is dealt with using a new approach. The aim of wind farm layout optimization is to maximize the output power of a wind farm considering the wake losses. Layout optimization minimizes the wake losses regarding the location of the turbines. Three different wind scenarios with different wind direction angles, wind direction blowing probabilities, and Weibull distribution parameters are assumed. Since, the problem is nonlinear and constrained, imperialist competitive algorithm is used as a modern and powerful algorithm for continuous optimization problems. The optimization outcomes indicate that imperialist competitive algorithm yields promising results.

[1]  Alireza Emami,et al.  New approach on optimization in placement of wind turbines within wind farm by genetic algorithms , 2010 .

[2]  Serap Ulusam Seçkiner,et al.  Wind farm layout optimization using particle filtering approach , 2013 .

[3]  Feng Wang,et al.  Modeling and simulation of optimal wind turbine configurations in wind farms , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.

[4]  Serap Ulusam Seçkiner,et al.  Design of wind farm layout using ant colony algorithm , 2012 .

[5]  D. A. Spera,et al.  Method for Evaluating Wind Turbine Wake Effects on Wind Farm Performance , 1985 .

[6]  N. Jensen.  A note on wind generator interaction , 1983 .

[7]  M. Y. Hussaini,et al.  Placement of wind turbines using genetic algorithms , 2005 .

[8]  Hou-Sheng Huang,et al.  Distributed Genetic Algorithm for Optimization of Wind Farm Annual Profits , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[9]  Javier Serrano González,et al.  Optimization of wind farm turbines layout using an evolutive algorithm , 2010 .

[10]  Caro Lucas,et al.  Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement , 2008, ICCSA.

[11]  Christopher N. Elkinton,et al.  An Analytical Framework for Offshore Wind Farm Layout Optimization , 2007 .

[12]  A. Khairuddin,et al.  A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO) , 2010, 2010 Conference Proceedings IPEC.

[13]  E. Pyrgioti,et al.  Optimal placement of wind turbines in a wind park using Monte Carlo simulation , 2008 .

[14]  Carlo Poloni,et al.  Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm , 1994 .

[15]  Jun Wang,et al.  Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[16]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[17]  Manuel Valenzuela-Rendón,et al.  Linear Wind Farm Layout Optimization through Computational Intelligence , 2009, MICAI.

[18]  Andrew Kusiak,et al.  Design of wind farm layout for maximum wind energy capture , 2010 .

[19]  Caro Lucas,et al.  Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process , 2008, Int. J. Intell. Comput. Cybern..