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Numerical Simulation for Transformer Winding Thermal Analysis Between Detailed Winding and Simplified Winding Models

EasyChair Preprint no. 4596

18 pagesDate: November 18, 2020

Abstract

Ability to predict and measure the hot-spot temperature is undoubtfully a key in developing thermal quality of transformer design. The hot-spot temperature is a very important parameter in transformer load management and a significant parameter to determine the thermal performances and loss of life prediction. Thermal-hydraulic network model (THNM) and CFD are very useful methods to model the temperature distribution inside the transformer winding. Performance-wise the CFD is far more advanced in providing detail study, but there are drawbacks such as complexity, very time consuming and very costly. This paper presents a two-dimensional (2D) axis symmetric CFD winding simplified model for both small distribution and medium power transformers. They are developed in favor to reduce model’s geometric complexity. The cooling effects of natural oil convection are observed on both oil flow and thermal distributions. Results show the SM is not recommended for detailed thermal and flow studies on the medium power transformer (disc winding), but it’s a good approach for small distribution transformer (layer winding). However, the SM is recommended during the early design stage for both transformers due to small variation in average winding temperature.

Keyphrases: Computational Fluid Dynamic (CFD), disc winding, hot spot temperature, Layer winding, natural oil convection, transformer

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:4596,
  author = {Zulkifli Ibrahim and Mohd Zainal Abidin Ab. Kadir and Norhafiz Azis and Jasronita Jasni and Muhammad Hakirin Roslan},
  title = {Numerical Simulation for Transformer Winding Thermal Analysis Between Detailed Winding and Simplified Winding Models},
  howpublished = {EasyChair Preprint no. 4596},

  year = {EasyChair, 2020}}
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