一种用于变压器故障诊断的卷积神经网络优化方法

doi:10.16180/j.cnki.issn1007-7820.2023.12.011

Abstract

Abstract:

Traditional fault diagnosis methods have disadvantages such as incomplete coding and overly absolute coding boundaries, which are difficult to meet the actual needs of power grid operation and maintenance. Using the gas generated when a power transformer fails to diagnose the transformer fault is currently a popular research area for smart grid condition detection. However, the frequency of various types of faults in transformers varies greatly, which may result in incomplete fault sample information and insufficient data, and the traditional convolutional neural network models have problems such as unstable training process, low training accuracy and long time. Based on the transformer fault diagnosis technology of one dimensional convolutional neural network, this study proposes a new method of data enhancement while keeping the original data features unchanged, transforming expanded one dimensional data into two dimensional pictures to input into the two dimensional convolutional neural network diagnosis model, and improve the Adam optimization algorithm in the convolutional neural network model architecture. Diagnostic results indicate that the accuracy of network training reaches 96.20%. At the same time, it has higher convergence speed and generalization ability than the traditional one dimensional convolutional neural network fault diagnosis method(92.12%).

Key words: power transformer, DGA, fault diagnosis, machine learning, data enhancement, two dimensional, neural network, Adam

CLC Number:

TM41

WANG Xuyang,YI Yingping,LI Tianfeng. A Convolutional Neural Network Optimization Method for Fault Diagnosis of Power Transformer[J].Electronic Science and Technology, 2023, 36(12): 79-86.

Figures/Tables 13

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References 18

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编号	网络层	卷积核大小	卷积核数目
C₁	卷积层	3×3	32
S₂	最大池化层1	2×2	32
C₃	卷积层2	1×1	64
S₄	最大池化层2	2×2	64
C₅	卷积层3	2×2	100
F₆	全连接层	100	1
L₇	输出层	6	1

气体含量/μL·L^-1					故障类型
H₂	CH₄	C₂H₆	C₂H₄	C₂H₂	故障类型
110.00	5.000	2.700	3.200	1.7	正常
14.67	3.680	10.540	2.710	0.2	低能放电
65.00	26.100	10.100	41.600	57.8	高能放电
218.00	120.700	126.600	142.100	0.9	中低温过热
26.50	298.000	0.000	69.300	8.6	高温过热
180.85	0.574	0.234	0.188	0.0	局部放电

编号	故障类型	编码
1	正常	000001
2	低能放电故障	000010
3	高能放电故障	000100
4	中低温过热故障	001000
5	高温过热故障	010000
6	局部放电故障	100000

模型	测试集诊断准确率/%			平均诊断准确率/%	训练时间/s
模型	T₁	T₂	T₃	平均诊断准确率/%	训练时间/s
1D CNN	90.37	91.94	92.12	91.48	266.78
2D CNN	93.26	95.38	96.20	94.94	211.38

场景	S₁	S₂
训练集精确度	95.37	97.22
测试集精确度	90.50	95.28

A Convolutional Neural Network Optimization Method for Fault Diagnosis of Power Transformer

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优化算法	参数
SGD	lr=0.1
RMSPROP	lr=0.1, β=0.2,ε=1×10^-6
Adam	lr=0.01,β₁=0.9 β₂=0.999,ε=1×10^-8

优化器类型	训练集准确率/%	测试集准确率/%	训练速度/s
SGD	94.45	94.24	0.040
RMSprop	100.00	92.82	0.369
Adam	100.00	95.86	0.474
改进Adam	100.00	96.20	0.326

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