Abstract: Focusing on the slow speed problem of existing brushless synchronous generator rotating rectifier fault recognition methods, this paper presents a fast recognition technique based on improved extreme learning machine (ELM). The chicken swarm optimization (CSO) is used to optimize the parameters of ELM, and hence, an optimized model of ELM can be achieved, and then applied it to rotating rectifier faults recognition of brushless synchronous generator. Experimental results show that, the optimized ELM can achieve good diagnosis performance and high classification speed. The presented method can be considered to the application of brushless synchronous generator rotating rectifier faults recognition and localization.

Key words: brushless synchronous generator, rotating rectifier, fault recognition, chicken swarm optimization, extreme learning machine