Robust aerodynamic design of variable speed wind turbine rotors

This study focuses on the robust aerodynamic design of the bladed rotor of small horizontal axis wind turbines. The optimization process also considers the effects of manufacturing and assembly tolerances on the yearly energy production. The aerodynamic performance of the rotors so designed has reduced sensitivity to manufacturing and assembly errors. The geometric uncertainty affecting the rotor shape is represented by normal distributions of the pitch angle of the blades, and the twist angle and chord of their airfoils. The aerodynamic module is a blade element momentum theory code. Both Monte Carlo-based and the Univariate ReducedQuadrature technique, a novel deterministic uncertainty propagationmethod, are used. The performance of the two approaches is assessed both interms of accuracy and computational speed. The adopted optimization method is based on a hybrid multi-objective evolutionary strategy. The presented results highlight that the sensitivity of the yearly production to geometric uncertainties can be reduced by reducing the rotational speed and increasing the aerodynamic blade loads.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[2]  D. Xiu,et al.  Modeling uncertainty in flow simulations via generalized polynomial chaos , 2003 .

[3]  Massimiliano Vasile,et al.  An Inflationary Differential Evolution Algorithm for Space Trajectory Optimization , 2011, IEEE Transactions on Evolutionary Computation.

[4]  Marin D. Guenov,et al.  Comparative Analysis of Uncertainty Propagation Methods for Robust Engineering Design , 2007 .

[5]  Marin D. Guenov,et al.  Novel Uncertainty Propagation Method for Robust Aerodynamic Design , 2011 .

[6]  Xiaoping Du,et al.  Efficient Uncertainty Analysis Methods for Multidisciplinary Robust Design , 2002 .

[7]  Edmondo Minisci,et al.  Minimum-Fuel/Minimum-Time Maneuvers of Formation Flying Satellites , 2003 .

[8]  Robert H. Leary,et al.  Global Optimization on Funneling Landscapes , 2000, J. Glob. Optim..

[9]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[10]  S.S. Venkata,et al.  Wind energy explained: Theory, Design, and application [Book Review] , 2003, IEEE Power and Energy Magazine.

[11]  Carlos M. Fonseca,et al.  Multi-objective evolutionary algorithm for land-use management problem , 2007 .

[12]  P. A. Newman,et al.  Approach for uncertainty propagation and robust design in CFD using sensitivity derivatives , 2001 .

[13]  A. Toffolo,et al.  Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation , 2002 .

[14]  Edmondo A. Minisci,et al.  MOPED: A Multi-objective Parzen-Based Estimation of Distribution Algorithm for Continuous Problems , 2003, EMO.

[15]  Jon C. Helton,et al.  Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems , 2002 .

[16]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[17]  Kalyanmoy Deb,et al.  Scope of stationary multi-objective evolutionary optimization: a case study on a hydro-thermal power dispatch problem , 2008, J. Glob. Optim..

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..