Pregled bibliografske jedinice broj: 1196453
Machine Learning in Power System Dynamic Security Assessment
Machine Learning in Power System Dynamic Security Assessment // Energies, 15 (2022), 11; 1-3 doi:10.3390/en15113962 (nije recenziran, uvodnik, ostalo)
CROSBI ID: 1196453 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Machine Learning in Power System Dynamic Security Assessment
Autori
Sarajcev, Petar
Izvornik
Energies (1996-1073) 15
(2022), 11;
1-3
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, uvodnik, ostalo
Ključne riječi
electrical power system ; transient stability assessment ; artificial inteligence ; wide-area measurement
Sažetak
This Special Issue will deal with novel approaches to the power system dynamic security assessment, and related power disturbance issues, which are based on the applications of machine learning, deep learning, and reinforcement learning techniques. It will also deal with problems related to advanced data acquisition (wide-area measurement systems) and data-sets preparation (statistical processing, features engineering, encoding, embedding).
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
IP-2019-04-7292 - Simulator poremećaja u elektroenergetskom sustavu i kalibrator nesinusnih napona i struja (SIMPES) (Petrović, Goran, HRZZ - 2019-04) ( CroRIS)
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split
Profili:
Petar Sarajčev
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
