Jialing Li

Dr. Jialing Li


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 September 2025 in Energies
Verified
Journal Article
Open access
PDF file
YHYujia Hu
Yujia Hu
LWLi Wang
Li Wang
JLJialing Li
Jialing Li
HWHuiying Weng
Huiying Weng
ZZZhiyao Zheng
Zhiyao Zheng
FZFan Zhang
Fan Zhang
The temperature rise test for transformers is time-consuming, energy-intensive, and has low detection efficiency. To improve the efficiency of the temperature rise test and reduce energy consumption, this paper proposes a temperature rise prediction method for oil-immersed transformer windings. This method is based on identifying the parameters of a thermal circuit model. Firstly, a fifth-order thermal circuit model of oil-immersed transformers is put forward. Then, based on a two-hour temperature rise curve, the thermal capacity and resistance model is identified through genetic algorithms. The obtained parameters are used to compute the temperature rise curve, steady-state average temperature rise, and top oil temperature rise. The results show that the heat capacities of the low-voltage (LV) winding, high-voltage (HV) winding, oil tank, and oil of a 400 kVA transformer are approximately 50 kJ/K, 75 kJ/K, 320 kJ/K, and 90 kJ/K, respectively. Additionally, the thermal resistances from the LV winding to oil, HV winding to oil, oil tank, and air are about 8 mK/W, 5 mK/W, 1 mK/W, and 11 mK/W, respectively. When the transformer capacity increases, the heating power of the windings escalates, and the oil resistance of HV windings decreases from 8 mK/W for a 400 kVA capacity to 5 mK/W for an 800 kVA capacity. The absolute prediction error for transformers of 400 kVA, 630 kVA, and 800 kVA is 2.9 °C. These findings can facilitate the swift detection and assessment of the winding temperature rise in oil-immersed transformers.