Research on transformer fault diagnosis method based on improved TCN model

doi:10.3969/j.issn.2097-0706.2024.11.005

Abstract

Abstract:

Transformer diagnosis methods based on traditional machine learning have been limited by low accuracy， single-source data， and a scarcity of fault samples. This paper proposes a transformer fault diagnosis model utilizing multi-source heterogeneous data fusion and the Mud Ring Algorithm （MRA） to optimize the Temporal Convolutional Network （TCN）. Oil chromatography data， infrared high-voltage bushing detection images， ultrasonic discharge detection images， and ultra-high frequency partial discharge detection images were selected as input information for the transformer fault diagnosis model. The Informer network and ResNet （Residual Network） were applied to extract and learn features from different data types， followed by feature fusion of multi-type data. The MRA algorithm was used to optimize the parameters of the TCN network， and the integrated results were used for fault classification. Experimental results showed that the proposed method achieved a Nash efficiency coefficient of 0.82 and an accuracy of 94.83%， with faster convergence， demonstrating its effectiveness in enhancing transformer fault diagnosis performance.

Key words: transformer, fault diagnosis, mud ring algorithm, multi-source heterogeneous data fusion, temporal convolutional network

CLC Number:

TM41

XU Bo, WEI Yijun, DENG Fangming. Research on transformer fault diagnosis method based on improved TCN model[J]. Integrated Intelligent Energy, 2024, 46(11): 38-45.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Oil chromatography data preprocessing

日期	$φ$ （H₂）/ (μL $⋅$ L^-1)	$φ$ (CO)/ (μL $⋅$ L^-1)	$φ$ (CO₂)/ (μL $⋅$ L^-11)	$φ$ (CH₄)/ (μL $⋅$ L^-1)	$φ$ (C₂H₆)/ (μL $⋅$ L^-1)	$φ$ (C₂H₄)/ (μL $⋅$ L^-1)	$φ$ (C₂H₂)/ (μL $⋅$ L^-1)	$φ$ (THC)/ (μL $⋅$ L^-1)	$w$ (H₂O)/ (mg $⋅$ kg^-1)
2023-07-10	150.75	157.56	2 305.79	10.40	1.70	1.58	0.87	15.55	8.50
2023-07-24	194.24	185.34	2 470.92	11.12	2.07	1.58	0.92	15.69	12.90
2023-07-31	209.96	176.00	3 235.13	11.73	2.58	1.90	3.36	19.57	13.40
2023-08-02	231.70	207.86	3 606.76	12.73	3.27	2.12	3.92	22.04	10.70
2023-08-07	187.64	201.50	2 463.52	11.21	3.24	1.98	1.22	17.65	12.80
2023-08-09	178.61	200.00	1 873.92	10.78	1.80	1.32	0.36	14.28	19.00
2023-08-14	157.10	178.46	1 968.24	10.42	3.06	1.67	0.41	15.56	6.80
2023-08-16	188.72	176.23	1 915.65	11.06	1.62	1.43	0.41	14.52	13.60
2023-08-21	178.92	197.50	2 386.50	11.06	2.18	1.61	0.00	14.85	8.20
2023-08-24	163.61	175.78	1 922.36	10.09	2.37	2.18	0.42	15.06	18.10
2023-08-30	170.59	188.61	1 902.63	10.83	1.80	1.30	0.34	14.26	10.00
2023-09-06	202.30	199.62	2 798.32	11.46	2.36	1.80	1.43	17.05	11.30
2023-09-13	174.64	167.46	2 604.40	10.53	2.84	1.56	1.32	16.25	7.70
2023-09-21	202.76	186.22	2 761.99	11.94	2.46	1.99	1.54	17.93	10.10
2023-09-27	196.88	177.07	2 890.30	11.81	1.90	1.76	1.76	17.23	7.70

Table 1

Fig.3

Fig.4

Fig.5

Table 2

Table 3

Fig.6

Table 4

Comparison of training data

方案	Huber	$P$ /%	$R$ /%	$A$ /%	$N$
1	0.34	82.32	83.51	79.92	0.46
2	0.18	89.65	88.92	89.72	0.64
3	0.24	87.16	86.61	86.34	0.57
4	0.15	91.68	92.52	94.83	0.82
5	0.36	80.16	79.18	75.50	0.41

Table 4

Fig.7

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方案	输入数据集	预测模型
1	仅DGA数据	TCN+MRA
2	DGA+IHVBDI+UDDI+UHFPDDI数据	TCN
3	DGA+IHVBDI数据	TCN+MRA
4	DGA+IHVBDI+UDDI+UHFPDDI数据	TCN+MRA
5	仅DGA数据	TCN