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

Long Wang, Ran Han, Tongguang Wang and Shitang Ke
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Long Wang: Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Ran Han: Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Tongguang Wang: Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Shitang Ke: Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Energies, 2018, vol. 11, issue 5, 1-19

Abstract: 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.

Keywords: wind turbine design; convergence performance; optimization efficiency; uniform decomposition; positive-gradient differential evolution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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