This paper presents a simukaneous multi- objective optimization of a direct-drive permanent magnet synchronous generator and a three-blade horizontal-axis wind turbine for a large scale wind energy conversion system. ...This paper presents a simukaneous multi- objective optimization of a direct-drive permanent magnet synchronous generator and a three-blade horizontal-axis wind turbine for a large scale wind energy conversion system. Analytical models of the generator and the turbine are used along with the cost model for optimization. Three important characteristics of the system i.e.,the total cost of the generator and blades, the annual energy output and the total mass of generator and blades are chosen as objective functions for a multi-objective optimization. Genetic algorithm （GA） is then employed to optimize the value of eight design parameters including seven generator parameters and a turbine parameter resulting in a set of Pareto optimal solutions. Four optimal solutions are then selected by applying some practical restrictions on the Pareto front. One of these optimal designs is chosen for finite element verification. A circuit-fed coupled time stepping finite element method is then performed to evaluate the no-load and the full load performance analysis of the system including the generator, a rectifier and a resistive load. The results obtained by the finite element analysis （FEA） verify the accuracy of the analytical model and the proposed method.展开更多
文摘This paper presents a simukaneous multi- objective optimization of a direct-drive permanent magnet synchronous generator and a three-blade horizontal-axis wind turbine for a large scale wind energy conversion system. Analytical models of the generator and the turbine are used along with the cost model for optimization. Three important characteristics of the system i.e.,the total cost of the generator and blades, the annual energy output and the total mass of generator and blades are chosen as objective functions for a multi-objective optimization. Genetic algorithm （GA） is then employed to optimize the value of eight design parameters including seven generator parameters and a turbine parameter resulting in a set of Pareto optimal solutions. Four optimal solutions are then selected by applying some practical restrictions on the Pareto front. One of these optimal designs is chosen for finite element verification. A circuit-fed coupled time stepping finite element method is then performed to evaluate the no-load and the full load performance analysis of the system including the generator, a rectifier and a resistive load. The results obtained by the finite element analysis （FEA） verify the accuracy of the analytical model and the proposed method.
