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Journal of Solar Energy Engineering
Article . 2002 . Peer-reviewed
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Optimal Design of Horizontal-Axis Wind Turbines Using Blade-Element Theory and Evolutionary Computation

Authors: BENINI, ERNESTO; TOFFOLO, ANDREA;

Optimal Design of Horizontal-Axis Wind Turbines Using Blade-Element Theory and Evolutionary Computation

Abstract

This paper describes a multi-objective optimization method for the design of stall-regulated horizontal-axis wind turbines. Two modules are used for this purpose: an aerodynamic model implementing the blade-element theory and a multi-objective evolutionary algorithm. The former provides a sufficiently accurate solution of the flow field around the rotor disc; the latter handles the decision variables of the optimization problem, i.e., the main geometrical parameters of the rotor configuration, and promotes function optimization. The scope of the method is to achieve the best trade-off performance between two objectives: annual energy production per square meter of wind park (to be maximized) and cost of energy (to be minimized). Examples of the best solutions found by the method are described and their performance compared with those of commercial wind turbines.

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Italy
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
128
Top 10%
Top 1%
Average