Wide & Deep Machine Learning Model for Transformer Health Analysis (CROSBI ID 678542)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Sarajcev, Petar ; Jakus, Damir ; Nikolic, Matej
engleski
Wide & Deep Machine Learning Model for Transformer Health Analysis
Transformer health index (HI) is a powerful tool for quantifying the overall health of a power transformer, due to the fact that it appraises its condition based on different criteria that are related (often in complex ways) to the long-term degradation factors that cumulatively lead to its end-of-life. Several authors have proposed different approaches to the HI calculation, e.g., analytical expressions, binary logistic regression, fuzzy logic models, support vector machines, and artificial neural networks. This paper proposes using Bayesian "Wide & Deep" machine learning model for the HI calculation, where the wide model part is the Bayesian ordered robust "probit" regression, while the deep part is the Bayesian artificial neural network. Both model parts are trained simultaneously within the Bayesian setting, using the so-called "joint learning" process with a Markov-chain Monte Carlo algorithm. Model is demonstrated using the actual transformer data.
Artificial neural network ; Bayesian learning ; Health Index ; Joint learning ; Probit regression ; Transformer
IEEE Catalog Number: CFP19F09‐USB ; ISBN 978‐953‐290‐089‐71
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Podaci o prilogu
S16-1570518679-1806
2019.
objavljeno
10.23919/SpliTech.2019.8783122
Podaci o matičnoj publikaciji
4th International Conference on Smart and Sustainable Technologies (SpliTech 2019)
Rodrigues, Joel J.P.C. ; Nizetic, Sandro
Split: Fakultet elektrotehnike, strojarstva i brodogradnje Sveučilišta u Splitu
Podaci o skupu
4th International Conference on Smart and Sustainable Technologies (SpliTech)
predavanje
18.06.2019-21.06.2019
Bol, Hrvatska; Split, Hrvatska
Povezanost rada
Elektrotehnika