Pregled bibliografske jedinice broj: 1092009
Ensemble Learning Approach to Power System Transient Stability Assessment
Ensemble Learning Approach to Power System Transient Stability Assessment // 5th International Conference on Smart and Sustainable Technologies (SpliTech 2020) / Rodrigues, Joel J.P.C. ; Nižetić, Sandro (ur.).
Split: Institute of Electrical and Electronics Engineers (IEEE), 2020. S23-1570621487, 6 doi:10.23919/splitech49282.2020.9243849 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1092009 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Ensemble Learning Approach to Power System Transient Stability Assessment
Autori
Kunac, A. ; Sarajcev, P.
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
5th International Conference on Smart and Sustainable Technologies (SpliTech 2020)
/ Rodrigues, Joel J.P.C. ; Nižetić, Sandro - Split : Institute of Electrical and Electronics Engineers (IEEE), 2020
ISBN
978-953-290-100-9
Skup
5th International Conference on Smart and Sustainable Technologies (SpliTech 2020)
Mjesto i datum
Split, Hrvatska, 23.09.2020. - 26.09.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Power system stability ; Transient stability assessment ; Machine learning ; Support vector machines ; Decision trees ; Ensemble learning
Sažetak
Power system transient stability assessment (TSA) can be represented as a machine learning (ML) binary classification problem. Network measurements data, collected from the distributed phasor measurement units during disturbances, constitute a large and imbalanced data set, on which the ML can be applied in order to learn to recognize the loss-of-stability from the various TSA incidents. This dataset, for actual power networks, contains hundreds of features, many of which can possibly be redundant and/or multi-correlated. This paper proposes an ensemble learning approach to the TSA classification problem, which includes a diverse set of base learners united by a voting ensemble. The imbalanced sample distribution and unequal misclassification costs are considered. Proposed approach also considers a feature selection as a pre-processing step, which is based on the importance analysis from different decision trees based models. Proposed ensemble learning model is applied on the IEEE New England 39-bus test case system. The obtained simulation results corroborate excellent performance and robustness of the proposed approach.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
Napomena
IEEE CatalogNumber: CFP19F09-USB
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
