Bayesian-neuro-fuzzy network based online condition monitoring system for resilient micro energy grid using FPGA
| Autor: | Hossam A. Gabbar, Yahya Koraz |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adaptive neuro fuzzy inference system
Smart grid Neuro-fuzzy Computer science 020209 energy Bayesian probability Real-time computing 0202 electrical engineering electronic engineering information engineering Condition monitoring 02 engineering and technology Fault (power engineering) Field-programmable gate array Fault detection and isolation |
| Zdroj: | 2017 IEEE 7th International Conference on Power and Energy Systems (ICPES). |
| DOI: | 10.1109/icpesys.2017.8215926 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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