基于多尺度极限融合网络的电力变压器故障诊断方法研究

doi:10.3969/j.issn.2097-0706.2022.09.011

摘要/Abstract

摘要：

针对固定参数数值的分类方法在电力变压器故障诊断上精度不高的问题,提出一种基于多尺度极限融合网络的诊断方法。该方法包含2个主要步骤。首先,基于不同参数尺度的极限学习机模型生成若干初始诊断结果。这些模型用于诊断同一组溶解性气体数据,获取不同的统计特性,为变压器故障诊断提供互补的统计信息。然后,采用基于决策级信息融合方法来融合不同尺度下诊断结果,提升故障诊断精度。共有487组试验数据用于模型训练和测试,结果表明：提出的故障诊断方法能精准地检测出6种常见变压器故障的类型,识别率为94%。相较于支持向量机和反向传播神经网络的诊断方法,诊断精度分别提高8%和13%,可满足电力企业对变压器故障诊断的应用需求。

关键词: 电力变压器, 故障诊断, 智能电网, 极限学习机, 信息融合, 多尺度极限融合网络

Abstract:

To cope with the low accuracy of the power transformer fault diagnosis taking a classification method with fixed-parameter algorithm,a new diagnosis method based on multi-scale extreme fusion network（MEFN）is proposed.There are two main steps in this method.Firstly,the initial diagnosis results are generated by extreme learning machine （ELM） models with different parameter scales.Models are made to analyze the same set of data about dissolved gas,and reflect different statistical characteristics,providing complementary statistical information to power transformer fault diagnosis.Then,the diagnostic results are fused by the decision-level information fusion algorithm,which can improve the accuracy of the power transformer fault diagnosis.Modeling training and test for the proposed method are made based on 487 sets of experimental data.The test results show that,MEFN can detect six types of common transformer faults with a high recognition accuracy of 94%.The recognition accuracy of MEFN is higher than that of support vector machine （SVM） and back-propagation （BP） neural network by 8% and 13%,respectively.The proposed method can meet the requirements on transformer fault diagnosis made by power companies.

Key words: power transformer, fault diagnosis,smart grid, ELM, information fusion, MEFN

中图分类号:

TK01：TM933

龙思成, 黄志鸿. 基于多尺度极限融合网络的电力变压器故障诊断方法研究[J]. 综合智慧能源, 2022, 44(9): 78-83.

LONG Sicheng, HUANG Zhihong. Research on fault diagnosis method of power transformers based on multi-scale extreme fusion network[J]. Integrated Intelligent Energy, 2022, 44(9): 78-83.

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气体体积分数	编码
气体体积分数	C₂H₂/C₂H₄	CH₄/H₂	C₂H₄/C₂H₆
<0.1	0	1	0
≥0.1~1.0	1	0	0
≥1.0~3.0	1	2	1
≥3.0	2	2	2

故障类型	气体编码
故障类型	C₂H₂/C₂H₄	CH₄/H₂	C₂H₄/C₂H₆
正常	0	0	0
局部放电	0或1	1	0
低压放电	1或2	0	0或1或2
高压放电	1	0	2
低温过载	0	0	1
中温过载	0	2	0
高温过载	0	2	1或2

样本序号	模型						真实故障类型
样本序号	ELM₅₀	ELM₁₀₀	ELM₁₅₀	ELM₂₀₀	ELM₂₅₀	MEFN	真实故障类型
1	PD	D₁	D₁	D₁	D₁	D₁	D₁
2	D₂	D₂	D₂	D₃	D₂	D₂	D₂
3	D₁	D₂	D₃	D₃	D₃	D₃	D₃
4	T₁	T₂	T₁	T₁	T₁	T₁	T₁
5	T₂	T₃	T₃	T₃	T₃	T₃	T₃
6	PD	PD	D₂	PD	PD	PD	PD
7	D₁	D₁	D₁	D₁	D₂	D₁	D₁

故障类型	BP分类器			SVM分类器			MEFN分类器
故障类型	N_total	N_right	准确率/%	N_total	N_right	准确率/%	N_total	N_right	准确率/%
PD	15	12	80.00	15	11	73.33	15	14	93.33
D₁	13	11	84.62	13	12	92.31	13	13	100.00
D₂	6	3	50.00	6	5	83.33	6	5	83.33
T₁	19	14	73.68	19	15	78.95	19	17	89.47
T₂	23	22	95.65	23	20	86.96	23	22	95.65
T₃	8	8	62.50	8	7	87.50	8	8	100.00
正常	16	13	81.25	16	15	93.75	16	15	93.75