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
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
13 November 2021
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).
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.
Text
Data driven transformer thermal model for condition monitoring
- Accepted Manuscript
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
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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
Author:
E. Simonson
Author:
G. Wilson
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