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Data driven transformer thermal model for condition monitoring

Data driven transformer thermal model for condition monitoring
Data driven transformer thermal model for condition monitoring
Condition monitoring of power transformers, which are key components of electrical power systems, is essential to identify incipient faults and avoid catastrophic failures. In this paper machine learning algorithms, i.e. nonlinear autoregressive neural networks and support vector machines, are proposed to model the transformer thermal behavior for the purpose of monitoring. The thermal models are developed based on the historical measurements from nine transformers comprised of two 180-MVA units, four 240-MVA units and three 1000-MVA units. The data consist of load profile, tap position, winding indicator temperature (WTI) measurement, ambient temperature, wind speed and solar radiation. The results are validated against field measurements, and it is clearly demonstrated that the alternative algorithms surpass the IEEE Annex G thermal model. An incipient thermal fault identification algorithm is then proposed and successfully used to identify an issue using measurements taken in the field. This algorithm could be used to alert the operator and plan intervention accordingly.
Artificial neural networks, Condition monitoring, Kernel, Oil insulation, Power transformer insulation, Support vector machines, Temperature measurement, Training, power transformer, transformer thermal model, winding temperature indicator
0885-8977
Doolgindachbaporn, Atip
61e63bb0-1269-406c-b29a-68d890e4a0c0
Callender, George
4189d79e-34c3-422c-a601-95b156c27e76
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Simonson, E.
4c8bcaf1-a5a0-4440-a8b8-67410233a9b4
Wilson, G.
8fd34f42-b70e-48a6-b256-cd6f938aa8c1
Doolgindachbaporn, Atip
61e63bb0-1269-406c-b29a-68d890e4a0c0
Callender, George
4189d79e-34c3-422c-a601-95b156c27e76
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Simonson, E.
4c8bcaf1-a5a0-4440-a8b8-67410233a9b4
Wilson, G.
8fd34f42-b70e-48a6-b256-cd6f938aa8c1

Doolgindachbaporn, Atip, Callender, George, Lewin, Paul, Simonson, E. and Wilson, G. (2021) Data driven transformer thermal model for condition monitoring. IEEE Transactions on Power Delivery. (doi:10.1109/TPWRD.2021.3123957).

Record type: Article

Abstract

Condition monitoring of power transformers, which are key components of electrical power systems, is essential to identify incipient faults and avoid catastrophic failures. In this paper machine learning algorithms, i.e. nonlinear autoregressive neural networks and support vector machines, are proposed to model the transformer thermal behavior for the purpose of monitoring. The thermal models are developed based on the historical measurements from nine transformers comprised of two 180-MVA units, four 240-MVA units and three 1000-MVA units. The data consist of load profile, tap position, winding indicator temperature (WTI) measurement, ambient temperature, wind speed and solar radiation. The results are validated against field measurements, and it is clearly demonstrated that the alternative algorithms surpass the IEEE Annex G thermal model. An incipient thermal fault identification algorithm is then proposed and successfully used to identify an issue using measurements taken in the field. This algorithm could be used to alert the operator and plan intervention accordingly.

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Data driven transformer thermal model for condition monitoring - Accepted Manuscript
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More information

Accepted/In Press date: 26 October 2021
Published date: 13 November 2021
Additional Information: Publisher Copyright: IEEE
Keywords: Artificial neural networks, Condition monitoring, Kernel, Oil insulation, Power transformer insulation, Support vector machines, Temperature measurement, Training, power transformer, transformer thermal model, winding temperature indicator

Identifiers

Local EPrints ID: 453227
URI: http://eprints.soton.ac.uk/id/eprint/453227
ISSN: 0885-8977
PURE UUID: 59fcff64-ed84-40c9-8364-7afe3f453757
ORCID for Paul Lewin: ORCID iD orcid.org/0000-0002-3299-2556

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Date deposited: 11 Jan 2022 17:40
Last modified: 17 Mar 2024 02:37

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Contributors

Author: Atip Doolgindachbaporn
Author: George Callender
Author: Paul Lewin ORCID iD
Author: E. Simonson
Author: G. Wilson

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