Fault Detection in DC Microgrids using Recurrent Neural Networks (CROSBI ID 707465)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Grcic, Ivan ; Pandzic, Hrvoje
engleski
Fault Detection in DC Microgrids using Recurrent Neural Networks
Reliable and accurate fault detection plays a crucial role in the microgrid operation by enabling an increased operational flexibility. Successful classification of events in complex microgrid systems requires advanced models of sufficient speed and accuracy. Deep neural networks meet these requirements, as they have demonstrated their capabilities in a wide range of applications. In particular, Recurrent Neural Networks (RNNs) are used for sequence learning, making them suitable for online fault detection. In this work, the RNN is applied to the time- domain signal to detect faults in a photovoltaic- based DC microgrid. The classifier successfully discriminates all events and proves its performance using various metrics.
fault detection, microgrid protection, deep learning, recurrent neural networks
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Podaci o prilogu
1-6.
2021.
objavljeno
10.1109/sest50973.2021.9543249
Podaci o matičnoj publikaciji
Vaasa: Institute of Electrical and Electronics Engineers (IEEE)
978-1-7281-7660-4
Podaci o skupu
4th International Conference on Smart Energy Systems and Technologies (SEST 2021)
predavanje
06.09.2021-08.09.2021
Vaasa, Finska