Pregled bibliografske jedinice broj: 1146492
Fault Detection in DC Microgrids using Recurrent Neural Networks
Fault Detection in DC Microgrids using Recurrent Neural Networks // 4th International Conference on Smart Energy Systems and Technologies (SEST 2021)
Vaasa: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 1-6 doi:10.1109/sest50973.2021.9543249 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1146492 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Fault Detection in DC Microgrids using Recurrent
Neural Networks
Autori
Grcic, Ivan ; Pandzic, Hrvoje
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
ISBN
978-1-7281-7660-4
Skup
4th International Conference on Smart Energy Systems and Technologies (SEST 2021)
Mjesto i datum
Vaasa, Finska, 06.09.2021. - 08.09.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
fault detection, microgrid protection, deep learning, recurrent neural networks
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
