关键词: 蚁群算法, 微分进化算法, 风电机组, 齿轮箱, 故障诊断

Abstract: A method based on BP neural networks trained by Ant Colony and Differential Evolution（AC-DE） algorithm is presented for fault diagnosis of wind turbine gearbox. The ant colony algorithm pheromone update mechanism for differential evolution algorithm which improves the convergence speed of differential evolution algorithm and using differential evolution individual ways to improve the ant colony algorithm update premature problem, it can reduce the risk of BP neural network algorithm falling into local minimum, improve the training efficiency, and speed up convergence by using AC-DE algorithm to optimize the weights and bias of BP neural network. The new algorithm is applied to wind turbine gearbox fault diagnosis forecast, the method is tested and results of fault diagnosis are right. The validity and practicability of BP neural network algorithm trained by AC-DE algorithm for the wind turbine gearbox fault diagnosis are proved.

Key words: ant colony algorithm, differential evolution algorithm, wind turbine, gearbox, fault diagnosis