Bayesian-neuro-fuzzy network based online condition monitoring system for resilient micro energy grid using FPGA

This study offers a fully automatic BBN-ANFIS-based model of online condition monitoring system for a resilient MEG using FPGA chips. A direct connection of a FPGA-ZedBoard with on-field sensors is proposed in this study. The design enables real-time concurrent measurements of MEG's fault diagnosis assessment by mean of a hybrid BBN and ANFIS based model. The BBN capable to form a consistent function of MEG's uncertainty based on experts contribution more than the data from measurement instruments (I&Cs). The proposed method shows a capability to predict failure-sources by fault-assessment computation process of observation symptoms. The proposed hybrid model aids engineering crew to make the optimum decision.