Uniform Decomposition and Positive-Gradient Differential Evolution for Multi-Objective Design of Wind Turbine Blade

Convergence performance and optimization efficiency are two critical issues in the application of commonly used evolution algorithms in multi-objective design of wind turbines. A gradient-based multi-objective evolution algorithm is proposed for wind turbine blade design, based on uniform decomposit...

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Bibliographic Details
Published in:Energies (Basel) Vol. 11; no. 5; p. 1262
Main Authors: Wang, Long, Han, Ran, Wang, Tongguang, Ke, Shitang
Format: Journal Article
Language:English
Published: Basel MDPI AG 2018
Subjects:
Algorithms
Decomposition
Efficiency
Optimization
Turbines
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Convergence performance and optimization efficiency are two critical issues in the application of commonly used evolution algorithms in multi-objective design of wind turbines. A gradient-based multi-objective evolution algorithm is proposed for wind turbine blade design, based on uniform decomposition and positive-gradient differential evolution. In the uniform decomposition, uniformly distributed reference vectors are established in the objective space to maintain population diversity so that the population aggregations, which are commonly observed for wind turbine blade design using gradient-free algorithms, are minimized. The positive-gradient differential evolution is introduced for population evolution to increase optimization efficiency by guiding the evolutionary process and significantly reducing searching ranges of each individual. Two-, three- and four-objective optimizations of 1.5 MW wind turbine blades reveal that the proposed algorithm can deliver uniformly distributed optimal solutions in an efficient way, and has advantages over gradient-free algorithms in terms of convergence performance and optimization efficiency. These advantages increase with the optimization dimension, and the proposed algorithm is more suitable for optimizations of small size populations, thus remarkably enhancing the design efficiency.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en11051262