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.
Highlights
As a key wind turbine component, the blade is a determining factor for energy harvesting efficiency, while producing and withstanding loads
We present a series of investigations for the two, three- and four-objective complex integrated designs of 1.5 MW wind turbine blades based on the proposed MODE/D&P algorithm
A gradient-based multi-objective evolution algorithm is proposed for wind turbine blade design
Summary
As a key wind turbine component, the blade is a determining factor for energy harvesting efficiency, while producing and withstanding loads. This makes it imperative that the blade should be designed based on its compatibility with the turbine, and wind turbine blade design is a process that includes multiple systems-level and component-level optimization objectives that are sometimes in conflict with one another [1]. In a typical design example [2], up to 32 design variables and 102 constraints are adopted in optimizing a wind turbine, resulting in an extremely challenging design process. Multi-objective design has been intensively studied and widely applied in wind turbine design [3,4]. Multi-objective optimizations stand in contrast to singular optimal solution
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