关键词: 不可行度, 低风速, 混合粒子群, 模拟退火, 约束主导

Abstract: To increase the power coefficient of blade at both rated and low wind conditions, the distribution of aerodynamic shape parameter at each blade element is studied. The wind turbine is typically operated under low wind conditions, however the influence has rarely been considered in blade optimization model. Hence, a nonlinear constrained optimization model with power coefficient under low wind conditions is introduced based on the blade element momentum theory and Wilson theory. Since the penalty factor is difficult to be determined in penalty function method when dealing with constraints, which may lead to prematurity phenomenon that the algorithm falls into local solution, a hybrid particle swarm algorithm combined with feasible dominated-constraint method is brought up. Based on particle swarm optimization theory and simulated annealing theory, the algorithm applies feasible dominated-constraint method to perform random survival selection under drifting annealing probability, which keeps the population diverse and can be evolving in more optimized direction, thus solves the problem that nonlinear constraint is difficult to handle and the population’s tendency to fall into local solution. So as to verify the algorithm, a nonlinear constrained optimization model for the 1.5MW wind turbine blade is established. The results indicate that the method can effectively handle nonlinear constraints, avoid prematurity of the process and increase the power coefficient of blade under rated and low wind conditions. It provides an excellent way of theoretical analysis to handle nonlinear constraints.

Key words: dominanted-constraint, hybrid particle swarm, infeasibility degree, low wind velocity, simulated annealing