Parameter Identification Method for Transformer Winding Equivalent Networks Based on Frequency Response Analysis: A Comparative Study
Transformers are essential power transformation equipment in power systems. Winding deformation is one of the main forms of transformer winding faults, which may cause performance degradation or even overall damage to the equipment. As the commonly used methods for diagnosing winding deformation, frequency response analysis (FRA) has problems such as the reliance on expert experience, insufficient universality for windings of different voltage levels and connection methods, etc. If the equivalent network parameters of the windings are identified based on the frequency response curve, the universality and effectiveness can be fundamentally guaranteed. This paper presents a comprehensive review and classification of domestic and international methods for parameter identification of transformer winding equivalent network based on FRA. It elaborates on the principles of parameter identification, as well as the correlation mechanism between frequency response curves and the equivalent network model of transformer windings. In addition, an evaluation is conducted on the principles, strengths, and key challenges of different algorithmic of parameter identification. Drawing upon existing research cases, practical recommendations are provided for the application of different algorithms. Finally, the challenges currently facing research in transformer winding parameter identification are analyzed, and potential future development trends are discussed.
Authors
- Zhaoyang Kang (ORCID: https://orcid.org/0000-0002-7434-0564)
- Junling Wang (ORCID: https://orcid.org/0000-0003-3663-7081)
- Shujun Liu (ORCID: https://orcid.org/0009-0001-9455-0382)
- Ran Zhu (ORCID: https://orcid.org/0000-0003-3986-0596)
- Yonghao Zhang (ORCID: https://orcid.org/0000-0002-0453-8903)
- Hongbin Wu
- Kaining Hou
- Hongshun Liu
- Fuqiang Ren
Institutions
- Shandong University (CN)
- Shandong Special Equipment Inspection Institute (CN)
Publication Details
- Journal
- Energies
- Published
- 2026-01-15
- DOI
- https://doi.org/10.3390/en19020427
- Primary Topic
- Power Transformer Diagnostics and Insulation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China