A novel encoding/decoding algorithm for constraint handling in a horizontal-axis wind turbine blade optimization process

• The encoding/decoding technique accelerates metaheuristic optimization processes. • The technique produces random design variables within their technical boundaries. • The method was applied to a 5-MW horizontal-axis wind turbine blade design. • The algorithm was successfully applied to both continuous and discrete variables. • The optimization of airfoil shape was more effective than other blade geometries. The aim of this research is to improve the speed of the wind turbine blade optimization process while consuming less computation power. The traditional process using the constraint optimization technique and metaheuristics (MHs) cannot simultaneously handle many design variables, which is usually addressed by simplifying the problem. In this study, a novel encoding/decoding algorithm for constraint handling (ENDEACH) was constructed to handle all design variables prior to the application of MHs. The encoding process introduces the decision variable vector containing random numbers and boundaries for all design variables. Then the decoding process generates design variables from the vector. The technique produces design variables that are random while being within technical boundaries. Therefore, unfeasible search areas were eliminated. The proposed hybrid optimization process was applied to a 5-MW horizontal-axis wind turbine blade design. 53 design variables, including chord length, twist angle, chord distribution slope, twist distribution slope, and airfoil shape, were treated by ENDEACH. Then, several up-to-date MHs were used to optimize for the maximum power coefficient of wind turbines. The performance gains of the MHs were indicated by the highest convergence rate and minimum fitness value. Moreover, the optimal rotor had a power coefficient of up to 0.4987, 7.28 % higher than that of the National Renewable Energy Laboratory’s 5-MW reference wind turbine. The encoding/decoding algorithm together with MHs significantly outperformed the traditional method with respect to the optimization process and wind turbine blade performance, thereby enabling us to complete the optimization more quickly and obtain better results.