Abstract
This paper presents a multi-objective optimization model of a wind turbine blade based on blade's parameterized finite element model, where annual energy production and blade mass are the objective functions, and aerodynamic and structural parameters are the design variables. In this study, the maximum axial thrust, strain, displacement, and first-order natural frequency of blade are selected as constraints. A novel competitive-cooperative game method is proposed to obtain the optimal preference solution. In this method, a new exploration method of player's strategy space named `correlation analysis under fuzzy k-means clustering' is proposed, and the payoff functions are constructed according to competitive and cooperative behaviors. Two optimization schemes with preference objectives are obtained and all goals showed clear improvements over the initial solutions, and this method reveals the relationship between blade shape and desired performance. More deeply, dynamic sensitivities of various design variables to objective functions are obtained for different blade shapes.
Highlights
In multi-objective optimization issues, each objective restricts and influences each other
(2) The method brings out the corresponding relationships between blade shapes and solutions of two target preferences, and it is embodied by the fact that solution of XAEP and XMass corresponds to the different chord length distribution, twist angle distribution, spar cap layering parameters, and airfoil section coordinates
The results demonstrate that the twist angle between Z = 22.0 m and Z = 36.5 m and the chord length located at Z = 32 m have a significant influence on annual energy production (AEP) for the 21 different blade shapes
Summary
In multi-objective optimization issues, each objective restricts and influences each other. A. ESTABLISHMENT OF STRATEGY SPACE EXPLORATION METHOD Arguably, the first critical step in multi-objective game theoretic methods is decomposing design variables into strategy spaces owned by each player. One important contribution of this study is that a novel strategy space exploration method, called ‘correlation analysis under fuzzy k-means clustering’, is proposed based on fuzzy theory and data mining This method takes the fuzzy membership degree of design variables to the objective functions into account, so decomposition results of strategy spaces is better than k clustering method [19]. The remaining design variables can be decomposed into the strategy space using the proposed method, which can improve the efficiency of dividing the strategy space
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