CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance

doi:10.11930/j.issn.1004-9649.202305039

Abstract

Abstract:

In recent years, deep belief network (DBN) based transformer fault diagnosis methods have been developed. However, they share two prominent drawbacks, which are the low accuracy issue caused by the within-class imbalance of transformer faults samples and the artificial determination of the network parameters of deep belief network (DBN). In this paper, a transformer fault diagnosis method based on sample balance processing and improved DBN is proposed. Firstly, an improved K-means (IK-means) synthesis minority oversampling technique (SMOTE) algorithm is proposed to obtain within-class and between-class balanced fault samples. Then, the Tent chaotic map embedded chaotic hybrid pelican optimization algorithm (CHPOA) is developed to optimize the number of hidden layer nodes and reverse fine-tuning learning rate of DBN, and the CHPOA-DBN transformer fault diagnosis model is constructed. Finally, the classical oversampling algorithm, the classical fault diagnosis model and the proposed method are compared and analyzed, based on the experimental data, respectively. The results show that the fault diagnosis accuracy of the proposed method reaches 96.25 %, which provide an important reference for intelligent fault diagnosis under imbalanced fault samples of transformers.

Key words: transformer fault diagnosis, within-class imbalance, sample balance processing, Tent chaotic map, DBN network parameters optimization

Shuang WANG, Qian LUO, Bo TANG, Lan JIANG, Jin LI. CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance[J]. Electric Power, 2023, 56(10): 133-144.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202305039

https://www.electricpower.com.cn/EN/Y2023/V56/I10/133

Figures/Tables 14

References 42

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函数	函数类型	维度	搜索范围	理论最优值
f₁	单峰	30	[–100, 100]ⁿ	0
f₂	多峰	30	[–5.12, 5.12]ⁿ	0

函数	函数类型	维度	搜索范围	理论最优值
f₁	单峰	30	[–100, 100]ⁿ	0
f₂	多峰	30	[–5.12, 5.12]ⁿ	0

编号	特征	编号	特征
1	H₂	8	C₂H₄/C₂H₆
2	CH₄	9	C₂H₂/(C_xH_x)
3	C₂H₆	10	H₂/(H₂+C_xH_x)
4	C₂H₄	11	C₂H₄/(C_xH_x)
5	C₂H₂	12	CH₄/(C_xH_x)
6	CH₄/H₂	13	C₂H₆/(C_xH_x)
7	C₂H₄/C₂H₂	14	(CH₄+C₂H₄)/(C_xH_x)

编号	特征	编号	特征
1	H₂	8	C₂H₄/C₂H₆
2	CH₄	9	C₂H₂/(C_xH_x)
3	C₂H₆	10	H₂/(H₂+C_xH_x)
4	C₂H₄	11	C₂H₄/(C_xH_x)
5	C₂H₂	12	CH₄/(C_xH_x)
6	CH₄/H₂	13	C₂H₆/(C_xH_x)
7	C₂H₄/C₂H₂	14	(CH₄+C₂H₄)/(C_xH_x)

类型	原样本集	训练集	测试集
N	400	320	80
PD	120	96	24
D1	110	88	22
D2	289	231	58
T1	83	66	17
T2	125	100	25
T3	379	303	76
LDT	45	36	9
HDT	44	35	9
总计	1595	1275	320