Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models

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

doi:10.1109/cdc.2009.5399571

Cited by 35 publications

(14 citation statements)

References 6 publications

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“…In terms of algorithms as surveyed in Réthoré 2010, the majority of studies to date make use of a genetic algorithm to find the optimum turbine layout (Mosetti et al 1994;Elkinton et al 2006;Grady et al 2005;Kusiak and Song 2010;Rasuo and Bengin 2010;Wan et al 2009;Wang et al 2009 (Mustakerov and Borissova 2010). These studies focused primarily on optimization using a single turbine type across the entire wind plant.…”

Section: Wind Plant Layout Optimizationmentioning

confidence: 99%

Applications of Systems Engineering to the Research, Design, and Development of Wind Energy Systems

Dykes¹,

Meadows²,

Felker³

et al. 2011

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Executive SummaryOver the past 30 years, wind energy has evolved from a small industry active in a few countries to a large international industry involving major players in the manufacturing, development, and utility sectors. Coinciding with the industry growth, significant innovation in the technology has resulted in larger sized turbines with lower associated costs of energy and more complex designs in all subsystems-from the rotor to the drivetrain to the electronics and control systems. However, as the deployment of the technology grows and its role within the electricity sector has become more prominent, so have the expectations of the technology in terms of performance, reliability, and cost. For the industry to continue to succeed and become a sustainable source of electricity, innovation in wind energy technology must continue to improve performance and lower the cost of energy while supporting seamless integration of wind energy into the electric grid without creating significant negative impacts on local communities and environments. At the same time, the nature of the issues associated with wind energy design and development are noticeably more complex than in the past due to a variety of factors such as, for example, large turbines sizes, offshore deployment or complex terrains. Looking toward the future, the industry would benefit from an integrated approach that simultaneously addresses turbine design, plant design and development, grid interaction and operation, and mitigation of adverse community and environmental impacts. These activities must be integrated in order to meet this diverse set of goals while recognizing trade-offs that exist between them.In order to address these challenges, National Renewable Energy Laboratory (NREL) has embarked on the Wind Energy Systems Engineering (WESE) initiative to evaluate how methods of systems engineering can be applied to the research, design, and development of wind energy systems. Systems engineering is a field within engineering that has a long history of application to complex technical systems such as aerospace. As such, the field holds much potential for addressing critical issues that face the wind industry today. This paper represents a first step for understanding this potential and lays out a conceptual design for the development of a WESE framework and tool. It reviews systems engineering methods as applied to related technical systems and illustrates how these methods can be combined in a WESE framework to meet the research, design, and development needs for the future of the industry. Subsequent efforts will focus on developing and implementing a framework based on the conceptual design illustrated in the last chapter of this report.In general, systems engineering approaches have the following four characteristics: holistic, multidisciplinary, integrated/value-driven, and long-term/life-cycle oriented. The approach is holistic in that it considers the full technical system, including any number of performance criteria, as well as potential...

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Section: Wind Plant Layout Optimizationmentioning

confidence: 99%

Applications of Systems Engineering to the Research, Design, and Development of Wind Energy Systems

Dykes¹,

Meadows²,

Felker³

et al. 2011

View full text Add to dashboard Cite

show abstract

“…This approach has been further studied recently in [1] and [9]. It is also significant the work by Wang et al [10], where a new improved wind and turbine models have been considered within a genetic algorithm. The authors have shown that this new model is able to produce better results than previous approaches in the literature.…”

Section: Introductionmentioning

confidence: 96%

Optimal Evolutionary Wind Turbine Placement in Wind Farms Considering New Models of Shape, Orography and Wind Speed Simulation

Saavedra-Moreno

Salcedo‐Sanz

Paniagua-Tineo

et al. 2011

Advances in Computational Intelligence

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Abstract. In this paper we present a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms. We consider a realistic model for the wind farm, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Several experiments show that the proposed evolutionary approach obtains very good solutions which maximize power production, and takes into account the different constraints of the problem.

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“…However, there were only few studies on this technology. The initial studies partitioned a farm into several square blocks of the same size, and placed one turbine in the center of a suitable block [7][8][9][10]. The width of the block is usually 3 ∼ 5 times of the turbine diameter, which guarantees the safety of turbine operations.…”

Section: Introductionmentioning

confidence: 99%

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

Wan

Wang

Yang

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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In this paper, a particle swarm optimization algorithm with Gaussian mutations, denoted by GPSO, is proposed to solve constrained optimization problems. Two Gaussian mutation operators are employed to search the promising regions for better solutions. One operator is for the region between the personal best position and the global best one. The other operator is for the region around the global best position. The Gaussian mutations help the population jump out of local optima and find better solutions with more probability. The feasibility-based method compares the performance of different particles. Evaluated by three typical optimization problems, GPSO is more accurate, robust and efficient for locating global optima. The GPSO method is applied to a wind-farm micrositing problem. Simulation results demonstrate that the power generation of the wind farm is further improved while the execution time is substantially reduced.

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Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models

Cited by 35 publications

References 6 publications

Applications of Systems Engineering to the Research, Design, and Development of Wind Energy Systems

Applications of Systems Engineering to the Research, Design, and Development of Wind Energy Systems

Optimal Evolutionary Wind Turbine Placement in Wind Farms Considering New Models of Shape, Orography and Wind Speed Simulation

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

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