Towards optimal reliability-based design of wind turbines towers using artificial intelligence

Metaheuristic Optimization Techniques and Artificial Neural Networks (ANNs) have proven to be useful tools to find the best design solutions for different structural systems. However, the application of both techniques oriented toward the optimal design of wind turbine towers still requires further research. A methodology is presented to obtain the best design solutions for onshore wind turbine steel towers using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and ANNs. The proposed methodology is applied to steel towers with heights of 70, 75, 80, and 85 m. Case studies are assumed to be located in La Ventosa, Oaxaca. Three design objectives are defined: minimize the mass of the structure, maximize the structural reliability, and maximize the wind power. In order to avoid high computational costs, ANNs are trained and used to obtain the structural reliability index & beta;. First, the design variables and constraints are defined; subsequently, a metaheuristic swarm intelligence-based optimization (MOPSO) technique is implemented. As a result of the optimization process using the MOPSO algorithm, a Pareto Front that includes the best design solutions is efficiently obtained. Finally, graphs are generated in order to recommend initial parameters for pre-design of steel towers, satisfying three design objectives.