Optimized ELM-based Fault Diagnosis Method for Spare Power Automatic Switching System

The spare power automatic switching system is vital to guarantee the stability and security for the power supple in the electrical power system. Therefore, the fault diagnosis of the spare power automatic switching system is a hot research topic. In this paper, we mainly focus on the fault diagnosis method based on the extreme learning machine (ELM) for the spare power automatic switching system. Thereinto, the novel intelligent optimization algorithm, i.e., the firefly algorithm (FA), is used to optimize the ELM. Therefore, the proposed optimized-ELM model can effectively detect the faults in the spare power automatic switching system. In the simulation section, the fault diagnosis accuracy of the optimized-ELM model can reach to 97.85%. The simulation results prove the effectiveness of the optimized ELM of our paper.