Bibliographic Details
| Title: |
Improving IEC thermal model for oil natural air natural transformers using optimised parameters based on dynamic simulation |
| Authors: |
Lijing Zhang, Yingting Luo, Gehao Sheng, Zizhan Ni, Xiuchen Jiang |
| Source: |
High Voltage, Vol 9, Iss 1, Pp 217-229 (2024) |
| Publisher Information: |
Wiley, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Electricity, QC501-721 |
| More Details: |
Abstract Accurate assessment of hot‐spot temperature is essential for the safe operation of power transformers. Existing dynamic thermal models cannot estimate hot‐spot temperature accurately since some input parameters are roughly determined by transformer capacity and cooling mode while ignoring the effect of winding structure, tank dimensions, and material physical properties. To improve the accuracy of temperature assessment, empirical parameters of the IEC thermal model including thermal constants, winding and oil exponents are optimised with the help of numerical simulation in this article. Based on energy conservation and heat transfer theory, a computational fluid dynamic (CFD) model of a transformer in oil natural air natural (ONAN) cooling mode is established. This CFD model simulates the entity's structure, sizes, and multi‐stage heat dissipation processes realistically, so it can more precisely calculate the dynamic hot‐spot temperature. According to the simulated temperature curves at different operating conditions, the thermal constants and oil exponent are estimated using non‐linear regression, and the winding exponent is optimised using linear regression. A case study is conducted on an ONAN transformer. It shows the improved IEC model with optimised parameters can more accurately evaluate hot‐spot temperature, and the absolute error is decreased by 2.4 K (38.7%) compared with traditional thermal models. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2397-7264 |
| Relation: |
https://doaj.org/toc/2397-7264 |
| DOI: |
10.1049/hve2.12374 |
| Access URL: |
https://doaj.org/article/d5f5a3470fa4469cb4d1aa4cf49d5b0a |
| Accession Number: |
edsdoj.5f5a3470fa4469cb4d1aa4cf49d5b0a |
| Database: |
Directory of Open Access Journals |
