IREC2015 the Sixth International Renewable Energy Congress 2015
DOI: 10.1109/irec.2015.7110915
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Wind farm layout design using modified particle swarm optimization algorithm

Abstract: Wind energy has shown tremendous potential for power generation. The energy is generated by wind turbines placed in a wind farm. To extract maximum energy from these wind farms, one of the most important issues is an efficient layout of the farms. This layout governs the location of each turbine in the wind farm. Due to its complexity, the wind farm layout design problem is classified as a complex optimization problem. Several attempts have been made previously to come up with better approaches and algorithms … Show more

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Cited by 7 publications
(11 citation statements)
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“…This section presents the PSO algorithms for designing the wind farm layout. The first approach is adaptation of the basic PSO algorithm with random initial placement of the given number of turbines in the 10×10 grid positions, while the second approach was proposed by Rehman and Ali [23] that incorporates heuristic based initial placement of the given number of turbines in the 10×10 grid positions. The search space for PSO is a grid of 10×10.…”
Section: Particle Swarm Optimization Algorithms For Wind Farm Layout mentioning
confidence: 99%
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“…This section presents the PSO algorithms for designing the wind farm layout. The first approach is adaptation of the basic PSO algorithm with random initial placement of the given number of turbines in the 10×10 grid positions, while the second approach was proposed by Rehman and Ali [23] that incorporates heuristic based initial placement of the given number of turbines in the 10×10 grid positions. The search space for PSO is a grid of 10×10.…”
Section: Particle Swarm Optimization Algorithms For Wind Farm Layout mentioning
confidence: 99%
“…The MPSO algorithm [23] evolved from the basic PSO algorithm. Unlike the basic PSO algorithm, which may start with a set of random initial solutions, the MPSO algorithm uses seed solutions which allow the algorithm to converge faster to an optimal solution.…”
Section: Modified Particle Swarm Optimization Algorithmmentioning
confidence: 99%
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“…Many of these challenges can be classified as optimisation problems (Nesmachnow, 2014;Wang, Zhao and Ren, 2007;Rutenbar, 1989) and they exist in various fields and disciplines. A large number of these problems are prevalent in industries such as in control fields (Yan and Wang, 2015), clinical medicine and materials science (Lan et al, 2015), wind power energy generations (Rehman and Ali, 2015), manufacturing sectors (Liu et al, 2015), productions (Liu et al, 2014), processes (Singh et al, 2012), engineering sectors (Wu, 2012) and there are some other implementations which include missile evasion systems, trajectory planning and radar applications which can be found in the ministry of defence (MoD) (Lu, Miao and Li, 2013;Kumar and Singh, 2013). Pragmatically, such problems often involve making an almost impossible or impractical selection that gives a single global optimal solution which has the best fitness value to an optimisation problem.…”
Section: Introductionmentioning
confidence: 99%