Fault recognition method of smart grid data acquisition system based on FNN and sequential DS fusion

It is of significant practical importance to ensure the operational safety smart grid, which requires real-time fault diagnosis and identifying what causes it based on an enormous amount data. This article further studies intelligent fault-identification method combination multi-machine learning methods bases researching Fault Diagnosis Smart Grid Data Acquisition System. Firstly, we should apply statistical analysis feature extraction for Then, can use fuzzy neural network (FNN) calculate probability prediction power distribution stations, manufacturers operation businesses, membership function corresponding uncertainty. Secondly, makes Dempster/Shafer (DS) evidence sequential fusion realize fusion, gives decision criteria failure causes. Thirdly, a grid data-acquisition system established FNN DS Evidence Fusion. Finally, experimental results actual data show that new has very good application effect at cause identification.