Proof of the Concept of Detailed Dynamic Thermal-Hydraulic Network Model of Liquid Immersed Power Transformers
The paper presents a physics-based method to calculate in real time the distribution of temperature in the active part of liquid immersed power transformers (LIPT) in a transient thermal processes during grid operation. The method is based on the detailed dynamic thermal-hydraulic network model (THN...
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Published in | Energies (Basel) Vol. 16; no. 9; p. 3808 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
28.04.2023
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Subjects | |
Online Access | Get full text |
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Summary: | The paper presents a physics-based method to calculate in real time the distribution of temperature in the active part of liquid immersed power transformers (LIPT) in a transient thermal processes during grid operation. The method is based on the detailed dynamic thermal-hydraulic network model (THNM). Commonly, up to now, lumped models have been used, whereby the temperatures are calculated at a few points (top-oil and hot-spot), and the parameters are determined from basic or extended temperature-rise tests and/or field operation. Numerous simplifications are made in such models and the accuracy of calculation decreases when the transformer operates outside the range of tested values (cooling stage, loading). The dynamic THNM reaches the optimum of accuracy and simplicity, being feasible for on-line application. The paper presents fundamental equations of dynamic THNM, which are structurally different from static THNM equations. The paper offers the numerical solver for the case of a closed-loop thermosyphon. To apply the method for real transformer grid operation, there is a need to develop details as in static THNM, which has been used to calculate the distribution of the temperatures in LIPT thermal design. The paper proves the concept of dynamic THNM using the experimental results of a closed-loop thermosyphon small-scale model, previously published by authors from McGill University in 2017. The comparison of dynamic THNM with measurements on that model are presented in the paper. |
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ISSN: | 1996-1073 1996-1073 |
DOI: | 10.3390/en16093808 |