Ensemble Learning Approach to Power System Transient Stability Assessment (CROSBI ID 696449)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
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