Article
Parameter estimation for hot-spot thermal model of power transformers using unscented Kalman filters
Author/s | González Cagigal, Miguel Ángel
Rosendo Macías, José Antonio Gómez Expósito, Antonio |
Department | Universidad de Sevilla. Departamento de Ingeniería Eléctrica |
Publication Date | 2023 |
Deposit Date | 2023-06-28 |
Published in |
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Abstract | This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state ... This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available. |
Funding agencies | Centro de Desarrollo Industrial y Tecnológico de España Ministerio de Ciencia e Innovación (MICIN). España |
Project ID. | CER-20191019
TP-20210270 PID2021-124571OB-I00 |
Citation | González Cagigal, M.Á., Rosendo Macías, J.A. y Gómez Expósito, A. (2023). Parameter estimation for hot-spot thermal model of power transformers using unscented Kalman filters. Journal of Modern Power Systems and Clean Energy, 11 (2), 634-642. https://doi.org/10.35833/MPCE.2022.000439. |
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