A Self-adaptive CS Algorithm and Its Application in Fault Diagnosis of Wind Turbine Gearboxes

Abstract

Aiming at the problems of premature convergence of cuckoo search algorithm and difficulty in effectively identifying the fault modes of wind turbine gearboxes, an intelligent diagnosis technology based on BP neural network trained by the self-adaptive cuckoo search (SaCS-BP) algorithm is proposed. By constructing SaCS algorithm, the step size and discovery probability are adjusted adaptively, and a set of benchmark functions are employed to test the effectiveness of the proposed algorithm. Then, the fault diagnosis model of wind turbine gearbox is constructed by combining SaCS with BP neural network. Experimental results show that SaCS algorithm has better optimization accuracy and universality. Moreover, compared with BP neural network and BP network trained by cuckoo search algorithm (CS-BP), SaCS-BP method has the highest diagnostic accuracy, so as to realize the effective identification of the fault modes of wind turbine gearboxes.

Keywords

• Cuckoo search (CS);Self-adaptive;Wind turbine gearbox;BP neural network;Fault diagnosis