Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet

Michał Kunicki, Sebastian Borucki, Andrzej Cichoń and Jerzy Frymus
Additional contact information
Michał Kunicki: Institute of Electrical Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
Sebastian Borucki: Institute of Electrical Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
Andrzej Cichoń: Institute of Electrical Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
Jerzy Frymus: Tauron Dystrybucja S.A., 31-060 Kraków, Poland

Energies, 2019, vol. 12, issue 18, 1-17

Abstract: A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.

Keywords: power transformers; condition assessment; online monitoring; temperature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/18/3561/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/18/3561/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:18:p:3561-:d:268079

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().