Blade layers optimization of wind turbines using FAST and improved PSO Algorithm

Liao, Caicai; Zhao, Xiaolu; Xu, Jinglei

doi:10.1016/j.renene.2011.08.011

Cited by 58 publications

(37 citation statements)

References 5 publications

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“…In this paper, the inertia weight w is the same as it in [5], which decreases monotonically by logarithmic. This is expressed as follows:…”

Section: Optimization Methodsmentioning

confidence: 99%

“…The blade structural design is a multi-criteria constrained optimization problem [5,11]. The design requirements, such as blade/tower clearance limit, strain limit along the fiber direction, surface stress limit, and fatigue life time over 20 years should be well satisfied [6].…”

Section: Constraint Conditionsmentioning

confidence: 99%

“…A concise summary of the work most relevant to this paper is presented. The work in [5] uses the FAST software and an improved particle swarm optimization (PSO) algorithm to minimize the blade mass. The thickness and the location of layers in spar caps are selected as the design variables.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method

et al. 2012

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This paper presents an optimization method for the structural design of horizontal-axis wind turbine (HAWT) blades based on the particle swarm optimization algorithm (PSO) combined with the finite element method (FEM). The main goal is to create an optimization tool and to demonstrate the potential improvements that could be brought to the structural design of HAWT blades. A multi-criteria constrained optimization design model pursued with respect to minimum mass of the blade is developed. The number and the location of layers in the spar cap and the positions of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining the above method and design model under ultimate (extreme) flap-wise load conditions. The optimization results are described and compared with the original design. It shows that the method used in this study is efficient and produces improved designs.

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“…In this paper, the inertia weight w is the same as it in [5], which decreases monotonically by logarithmic. This is expressed as follows:…”

Section: Optimization Methodsmentioning

confidence: 99%

Section: Constraint Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method

et al. 2012

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“…The blade design is a multi-criteria constrained optimization problem, and the aerodynamic and structural requirements should be well satisfied [24,25]. In this paper, the following constraint conditions are mainly taken into account: the strain, the tip deflection, the vibration and the buckling Energies 2016, 9, 66 9 of 18 constraints.…”

Section: Constraint Conditionsmentioning

confidence: 99%

Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades

Zhu

Cai

2016

Energies

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Abstract:A procedure based on MATLAB combined with ANSYS is presented and utilized for the aerodynamic and structural integrated optimization design of Horizontal-Axis Wind Turbine (HAWT) blades. Three modules are used for this purpose: an aerodynamic analysis module using the Blade Element Momentum (BEM) theory, a structural analysis module employing the Finite Element Method (FEM) and a multi-objective optimization module utilizing the non-dominated sorting genetic algorithm. The former two provide a sufficiently accurate solution of the aerodynamic and structural performances of the blade; the latter handles the design variables of the optimization problem, namely, the main geometrical shape and structural parameters of the blade, and promotes function optimization. The scope of the procedure is to achieve the best trade-off performances between the maximum Annual Energy Production (AEP) and the minimum blade mass under various design requirements. To prove the efficiency and reliability of the procedure, a commercial 1.5 megawatt (MW) HAWT blade is used as a case study. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

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“…A Pareto Front (PF) is achieved through this compromise. Currently, multi-objective optimization of wind turbines is all achieved by evolution algorithms, including the hierarchical genetic algorithm [5], Pareto archived evolution strategy [6], strength Pareto evolutionary algorithm 2 [7,8], multi-objective genetic algorithm [9], non-dominated sorting genetic algorithm-II (NSGA-II) [10][11][12][13] and particle swarm optimization (PSO) [14,15]. These algorithms are categorized as gradient-free algorithms (GFAs) [16].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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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Blade layers optimization of wind turbines using FAST and improved PSO Algorithm

Cited by 58 publications

References 5 publications

Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method

Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method

Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades

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

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