基于人工鱼群算法和LS_SVM的变压器故障诊断

doi:10.16180/j.cnki.issn1007-7820.2020.11.007

Abstract

Abstract:

In view of the information uncertainty of transformer fault data and the low accuracy of traditional diagnostic methods, the combination of artificial fish swarm algorithm and least squares support vector machine is used to diagnose transformer fault. The DGA characteristic gas ratios of IECTC10 database is used as the input vectors, and the fault diagnosis model of transformers is designed based on LS_SVM. Meanwhile, the artificial fish swarm algorithm is utilized to optimize the parameters of the least squares support vector machine. Then, based on the diagnosis result, the multi-ratio characteristic parameter combination with the best classification effect is selected. The experimental verification results show that the accuracy of the proposed diagnostic method was up to 96.67%, and it has a higher accuracy rate of fault diagnosis.

Key words: least square support vector machine, fault diagnosis, artificial fish swarm algorithm, power transformer, DGA, IECTC10 database

CLC Number:

TP181

YANG Yu,ZENG Guohui,HUANG Bo. A Transformer Fault Diagnosis Method Integrating Artificial Fish Swarm Algorithm with Least Square Support Vector Machine[J].Electronic Science and Technology, 2020, 33(11): 36-40.

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References 16

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编号	DGA比值	编号	DGA比值
1	CH₄/C₂H₂	6	H₂/C₂H₆
2	C₂H₂/CH₄	7	H₂/C₂H₂
3	C₂H₄/CH₄	8	H₂/C₂H₄
4	CH₄/C₂H₆	9	C₂H₆/C₂H₄
5	C₂H₆/C₂H₂	10	H₂/CH₄

特征选择结果	特征组合
特征选择结果	1	2	3
DGA比值	H₂/C₂H₄	H₂/C₂H₄	H₂/C₂H₂
	H₂/C₂H₆	H₂/C₂H₂	C₂H₂/C₂H₄
	CH₄/C₂H₂	CH₄/C₂H₂	CH₄/C₂H₄
	C₂H₆/C₂H₄	CH₄/C₂H₆	CH₄/C₂H₆
	CH₄/C₂H₆	——	——

特征	DGA比值	特征	DGA比值
1	H₂/C₂H₄	4	C₂H₆/C₂H₄
2	H₂/C₂H₆	5	CH₄/C₂H₆
3	CH₄/C₂H₂

故障类型	训练集	测试集	类型编号
PD	14	4	1
D₁	25	2	2
D₂	25	11	3
T₁	16	6	4
T₂	22	7	5

实例编号	油中溶解气体体积分数/10^-6					诊断结果
实例编号	H₂	CH₄	C₂H₄	C₂H₆	C₂H₂	LS_SVM	本文方法	IEC三比值	实际故障
1	155.00	581.00	215.00	293.00	142.00	2	4	1	1
2	49.00	41.60	124.00	25.10	15.70	5	3	2	3
3	21.00	7.60	96.30	4.70	26.73	3	3	3	3
4	9.00	16.70	85.30	9.90	3.40	2	2	3	2
5	250.00	63.00	66.00	2.80	120.00	3	3	3	3
6	4.10	3.50	1.20	0.68	5.20	2	4	4	4
7	12.10	4.40	3.50	40.00	4.10	4	5	5	5
8	44.20	101.00	11.80	13.00	0.45	2	2	2	2
9	6.84	11.30	12.60	20.00	4.01	5	5	5	5
10	58.00	103.00	251.00	51.00	6.00	1	1	2	1
11	72.00	116.00	70.00	36.00	19.00	3	3	3	3
12	9.00	16.70	85.30	9.90	3.40	2	2	1	2

A Transformer Fault Diagnosis Method Integrating Artificial Fish Swarm Algorithm with Least Square Support Vector Machine

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