Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Ensemble Learning Approach to Power System Transient Stability Assessment (CROSBI ID 696449)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Kunac, A. ; Sarajcev, P. 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. doi: 10.23919/splitech49282.2020.9243849

Podaci o odgovornosti

Kunac, A. ; Sarajcev, P.

engleski

Ensemble Learning Approach to Power System Transient Stability Assessment

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.

Power system stability ; Transient stability assessment ; Machine learning ; Support vector machines ; Decision trees ; Ensemble learning

IEEE CatalogNumber: CFP19F09-USB

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

S23-1570621487

2020.

objavljeno

10.23919/splitech49282.2020.9243849

Podaci o matičnoj publikaciji

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)

978-953-290-100-9

Podaci o skupu

5th International Conference on Smart and Sustainable Technologies (SpliTech 2020)

predavanje

23.09.2020-26.09.2020

online

Povezanost rada

Elektrotehnika

Poveznice