Optimization of a low speed wind turbine using support vector regression

[摘要] NUMERICAL design optimization provides a powerful tool that assists designers inimproving their products. Design optimization automatically modifies importantdesign parameters to obtain the best product that satisfies all the design requirements.This thesis explores the use of Support Vector Regression (SVR) and demonstrates itsusefulness in the numerical optimization of a low-speed wind turbine for the power coe cient, Cp. The optimization design problem is the three-dimensional optimization ofa wind turbine blade by making use of four two-dimensional radial stations. The candidateairfoils at these stations are selected from the 4-digit NACA range. A metamodelof the lift and drag coe cients of the NACA 4-digit series is created with SVR by usingtraining points evaluated with XFOIL software. These SVR approximations are used inconjunction with the Blade Element Momentum theory to calculate and optimize the Cpvalue for the entire blade. The high accuracy attained with the SVR metamodels makesit a viable alternative to using XFOIL directly, as it has the advantages of being fasterand easier to couple with the optimizer. The technique developed allows the optimizationprocedure the freedom to select profiles, angles of attack and chord length fromthe 4-digit NACA series to find an optimal Cp value. As a result of every radial bladestation consisting of a NACA 4-digit series, the same lift and drag metamodels are usedfor each station. This technique also makes it simple to evaluate the entire blade asone set of design variables. The thesis contains a detailed description of the design andoptimization problem, the implementation of the SVR algorithm, the creation of the liftand drag metamodels with SVR and an alternative methodology, the BEM theory and asummary of the results.

[发布机构] Stellenbosch University