Fault Identification Method Based on BP Neural Network in Accelerator Distribution Network

Accelerator as a large scale of electrical facilities is trending towards high energy, high intensity and high reliability in the future. Next generation of accelerator will require unprecedented stability of distribution networks. Traditional monitoring methods have some limitations when detecting and analyzing subtle and complex power quality problems. A big data platform built for HIRFL(The Heavy Ion Research Facility in Lanzhou) distribution network integrates various big data technologies and combines historical data with online operational data of the distribution network. Based on this platform, this paper constructs, trains and deploys BP neural network models to identify faults in the distribution network. The results demonstrate that the models have outstanding capabilities of learning and generalization accompanied by accurate identification and classification of faults. This method is significant for the informatization and intelligence of future accelerator distribution networks.