| Title |
Parameter Estimation of Transformer Frequency Response Model |
| Authors |
Junhyun Im ; Yeunggurl Yoon ; Sungyun Choi |
| DOI |
http://doi.org/10.5207/JIEIE.2022.36.2.008 |
| Keywords |
High-frequency model; Machine learning; Power transformer; Parameter estimation; Random forest; Sweep frequency response analysis |
| Abstract |
Examining power transformer faults is crucial for maintaining the reliability of the power system. The most popular methods for detecting power transformer fault include thermal analysis, vibration analysis, partial discharge analysis, dissolved gas analysis(DGA), and sweep frequency response analysis(SFRA). Especially, the SFRA test is examined to detect transformer internal fault such as winding fault. Simulation-level frequency response analysis enables inspection of the power transformer before connecting to the grid. This paper proposes a parameter estimation method using machine learning for the power transformer frequency response equivalent model. |