Transformer fault diagnosis method based on NNTR-SMOTE and GA-XGBoost

doi:10.3969/j.issn.2097-0706.2024.01.010

Abstract

Abstract:

To address the low accuracy of transformer fault diagnosis caused by the insufficient number and uneven distribution of fault samples， a transformer fault diagnosis method based on nearest neighbour triangle regions synthetic minority oversampling technique （NNTR-SMOTE ）and genetic algorithm optimized extreme gradient boosting（GA-XGBoost）is proposed. Firstly， the transformer fault sample data are collected and standardized， and then balanced data are obtained by NNTR-SMOTE. Secondly， the feature data of dissolved gas are categorized by non-coding ratio method， and then fused by multi-dimensional scaling （MDS） method. Finally， a new transformer fault diagnosis model is constructed based on the XGBoost model optimized by GA. The experimental results show that the diagnostic accuracy of the proposed diagnosis method based on NNTR-SMOTE and GA-XGBoost reaches as high as 95.97%. This method not only solves the bias towards the majority class during diagnosis modelling， but also improves the diagnostic accuracy of the model， making it suitable for multi-classification fault diagnosis for transformers with unbalanced data.

Key words: transformer, fault diagnosis, unbalanced small sample, extreme gradient boosting, genetic algorithm

CLC Number:

TM411

WANG Lizhong, CHI Jianfei, DING Yeqiang, YAO Haiyan, TANG Zhipeng, WU Tongyu. Transformer fault diagnosis method based on NNTR-SMOTE and GA-XGBoost[J]. Integrated Intelligent Energy, 2024, 46(1): 84-93.

Figures/Tables 16

Fig.1

Fig.2

Table 1

Fusion matrix

项目	预测正类	预测负类
实际正类	$n T P$	$n F N$
实际负类	$n F P$	$n T N$

Table 1

Table 2

Table 3

Table 4

Fig.3

Table 5

Dimension reduction indicators of different dimensionality

降维指标	6维	5维	4维	3维	2维	1维
降维时间/s	2.46	2.48	2.53	2.57	2.59	2.64
$S s t r e s s$	0.014 7	0.015 3	0.016 4	0.017 8	0.019 1	0.021 6
Q_L	0.825 2	0.832 6	0.844 3	0.857 2	0.862 5	0.871 4
Q_G	0.403 2	0.416 4	0.422 0	0.430 2	0.438 5	0.449 2
Q_LG	0.614 2	0.624 5	0.633 2	0.643 7	0.650 5	0.660 3
准确率/%	91.84	92.36	93.52	95.45	95.97	90.63

Table 5

Table 6

Fig.4

Table 7

Table 8

Fig.5

Table 9

Fig.6

Table 10

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状态类型	样本数量	占比/%	类别标签
正常状态	91	21.41	1
中温过热	46	10.82	2
中低温过热	33	7.77	3
高温过热	53	12.47	4
放电兼过热	17	4.00	5
局部放电	29	6.82	6
低能放电	72	16.95	7
高能放电	84	19.76	8

特征编码	特征量	特征编码	特征量
1	CH₄/H₂	10	C₂H₄/THC
2	C₂H₂/H₂	11	C₂H₆/THC
3	C₂H₂/C₂H₄	12	C₂H₂/THC
4	C₂H₄/C₂H₆	13	(CH₄+C₂H₄)/THC
5	C₂H₆/CH₄	14	H₂/ALL
6	C₂H₂/CH₄	15	CH₄/ALL
7	C₂H₄/CH₄	16	C₂H₂/ALL
8	H₂/THC	17	C₂H₄/ALL
9	CH₄/THC	18	C₂H₆/ALL

运行状态	查全率/%	查准率/%	误报率/%	漏报率/%	Kappa系数	准确率/%
正常状态	94.4	89.5	5.6	10.5	0.954 2	95.97
中温过热	94.4	100.0	5.6	0
中低温过热	100.0	100.0	0	0
高温过热	94.7	100.0	5.3	0
放电兼过热	89.5	89.5	10.5	10.5
局部放电	100.0	100.0	0	0
低能放电	94.7	90.0	5.3	10.0
高能放电	100.0	100.0	0	0

采样方法	准确率/%	Kappa系数
NNTR-SMOTE	95.97	0.954 2
SMOTE-ENN	93.26	0.939 5
ADASYN	91.75	0.913 3
SMOTE	90.56	0.896 1
随机过采样	89.32	0.884 9
非均衡数据集	86.32	0.854 2

评价指标	MDS融合特征	PCA融合特征	LDA融合特征	LLE融合特征	18维联合特征
诊断准确率/%	95.97	92.02	91.30	89.13	87.68
Kappa系数	0.954 2	0.937 3	0.912 6	0.900 2	0.875 4