Research on Transformer Fault Diagnosis Method Based on Digital Twin

doi:10.16182/j.issn1004731x.joss.23-1402

Abstract

Abstract:

Aiming at the inability of existing intelligent algorithms for transformer fault diagnosis to quickly and efficiently identify transformer faults, resulting in fault misdetection and untimely detection, this paper proposes a transformer fault diagnosis method using the improved sparrow optimization algorithm to optimize the two-layer fault diagnostic model of XGBoost combined with the digital twin technology. The method adopts advanced sensors to collect oil and gas data and temperature data of the transformer, uses 5G module to transmit the real-time data to the digital twin system. The system monitors the temperature data in real-time by setting the equipment alarm threshold; optimizes the two-layer fault diagnostic model of XGBoost using the improved sparrow optimization algorithm to process real-time fault identification of the oil and gas data, and finally identifies and warns of faults by combining with the digital twin technology. Experimental results indicate that this method significantly enhances the efficiency and stability of fault identification and early warning, demonstrating substantial advantages compared to existing transformer fault diagnosis methods.

Key words: transformer, digital twins, fault diagnosis, real time monitoring, warn

CLC Number:

TP274

Jiang Lun, Wang Dajiang, Sun Wenlei, Bao Shenghui, Liu Han, Chang Saike. Research on Transformer Fault Diagnosis Method Based on Digital Twin[J]. Journal of System Simulation, 2025, 37(3): 775-790.

Figures/Tables 22

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Air Thermophysical Properties

T/℃	$ρ / (k g · m 3)$	$λ$ $/ [W · (m · K) - 1]$	К $/ (m 2 · s - 1)$	$ν / (m 2 · s - 1)$	$P r$ /(m/s)
-30	1.450	2.20×10^-2	14.9×10^-6	10.80×10^-6	0.723
-20	1.395	2.28×10^-2	16.2×10^-6	11.62×10^-6	0.716
-10	1.342	2.35×10^-2	17.4×10^-6	12.45×10^-6	0.710
0	1.293	2.44×10^-2	18.8×10^-6	13.28×10^-6	0.708
10	1.247	2.50×10^-2	20.0×10^-6	14.15×10^-6	0.706
20	1.206	2.57×10^-2	21.3×10^-6	15.07×10^-6	0.704
30	1.165	2.64×10^-2	22.9×10^-6	16.00×10^-6	0.702
40	1.127	2.76×10^-2	24.3×10^-6	16.96×10^-6	0.699

Table 1

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 2

Main Technical Parameters and Parameter Dimensions of Transformers

参数	铭牌值
额定电压/kV	10
额定输入功率/Hz	50
额定容量/kVA	30
冷却方式	ONAN
油箱尺寸/mm	$1 130 × 780 ×$ 1 010
额定输出功率/Hz	50
初级线圈/匝	1 000
次级线圈/匝	20
空载/W	100
负载/W	600

Table 2

Fig. 11

Fig. 12

Table 3

Table 4

Physical parameters of transformer oil

物性参数	矿物绝缘油
密度 $ρ / (k g · m - 3)$	1 098.72-0.712 T
导热系数k/(W· $m - 1 K - 1$ )	0.159-7.101 $× 10 - 5 T$
动力粘度 $μ k g · m - 1 · s - 1$	0.084 67-0.000 4 T+5 $× 10 - 7 T 2$
定压比热 $c p / (J · k g - 1 · K - 1)$	807.163+3.58 T

Table 4

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

Fig. 18

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构建	密度/(kg/m^-3)	导热系数/ (W·m^-1·k^-1)	比热容/ (J·kg^-1K^-1)
铁芯	7 650	45	460
绕组	8 900	385	390
油箱	7 850	50	485
变压器油	895	0.135	0.5

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