Particle swarm optimization based on Gaussian mutation and its application to wind farm micro-siting

Wan, Chunqiu; Wang, Jun; Yang, Geng; Zhang, Xing

doi:10.1109/cdc.2010.5716941

Cited by 13 publications

(11 citation statements)

References 14 publications

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“…The method of nesting two algorithms is well known in literature and there are a number of publications available [4][5]. Other implementation are of HMAs are such as a combination of two or more algorithms to attain a common objective are also discussed in [6][7][8][9].…”

Section: The Problem Space Is Non-linear and Totally Dependent Onmentioning

confidence: 99%

A New Hybrid Metaheuristic Algorithm for Wind Farm Micrositing

Massan

Wagan

Shaikh

2017

Mehran Univ. res. j. eng. technol.

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This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic Algorithm) for the solution of the WTO (Wind Turbine Optimization) problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (Differential Evolution Algorithm) and the FA (Firefly Algorithm). The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic Algorithm) used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together.

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Section: The Problem Space Is Non-linear and Totally Dependent Onmentioning

confidence: 99%

A New Hybrid Metaheuristic Algorithm for Wind Farm Micrositing

Massan

Wagan

Shaikh

2017

Mehran Univ. res. j. eng. technol.

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“…The global wind capacity grew by 7% within six months and by 27 % on an annual basis over the last five years [17]. Currently, there is a significant work around the world on wind turbine placement focused on collecting the maximum wind power by the turbines and maximizing the power generated by wind turbines [8][9] and [16].…”

Section: Lightning and Wind Turbinesmentioning

confidence: 99%

“…Researchers have paid attention to direct and indirect effect of lightning on wind turbines [1][2][3], and other researchers have investigated the protection of the wind turbine from lightning [4][5][6]. Optimal positioning of multiple windmills in a small footprint wind park or for maximum wind attraction [7] or maximizing the total output energy with minimum number of wind turbine used [8][9] have been considered in the past. However, not much has been studied about the optimization of wind turbine employment against lightning strokes.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm technique on wind turbine and sensitive equipment placement against lightning

Kazazi

Raahemifar

2013

2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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Lightning as a natural phenomenon represents directly and indirectly a risk for wind turbines and other objects as the transient voltages may reach dangerous levels for people, equipment, and transmission lines. It is important to consider lightning and its consequences on the placement of wind turbines, buildings, and other sensitive electrical installations.The objective of this study is to optimize the placement of a wind farm for minimum lightning occurrence on the turbines. The method is also applied to optimize the location of any other sensitive installation for minimum electromagnetic interference from lightning current in any geographic area. The process involves gathering accurate lightning data information for the interested area from North American Lightning Detection Network (NALDN) over a period of time and using the genetic algorithm optimization technique using MATLAB to determine the wind farm location and other sensitive equipment placement. It is found that genetic algorithm optimization technique produced results which are very consistent considering different scenarios. The findings in this study could help wind farm designers in addition to other criteria for energy collecting efficiency of wind turbines in order to determine the best location of wind farms.Index Terms-Electromagnetic transient, lightning protection, wind turbine placement, genetic algorithm, induced voltage.

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“…The first approach proposed in [1]- [6] to reduce the energy cost is based on turbine layout optimization, i.e., the optimal type and the number and placements of WTs. The criteria of this optimization are related to the characteristics of the site (the ground type and water depth) and wind speed (direction and intensity).…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Connection Topology of an Offshore Wind Farm Network

Dahmani¹,

Bourguet²,

Machmoum³

et al. 2015

IEEE Systems Journal

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In this paper, an approach based on a genetic algorithm is presented in order to optimize the connection topology of an offshore wind farm network. The main objective is to introduce a technique of coding a network topology to a binary string. The first advantage is that the optimal connections of both middleand high-voltage alternating-current grids are considered, i.e., the radial clustering of wind turbines, the number and locations of the offshore electrical substations, and the number of high-voltage cables. The second improvement consists of removing infeasible network configurations as designs with crossing cables and thereby reduces the search space of solutions.Index Terms-Genetic algorithm (GA), network planning, offshore wind farm (OWF), optimization, Prim's algorithm.

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Particle swarm optimization based on Gaussian mutation and its application to wind farm micro-siting

Cited by 13 publications

References 14 publications

A New Hybrid Metaheuristic Algorithm for Wind Farm Micrositing

A New Hybrid Metaheuristic Algorithm for Wind Farm Micrositing

Genetic algorithm technique on wind turbine and sensitive equipment placement against lightning

Optimization of the Connection Topology of an Offshore Wind Farm Network

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