多属性融合的电力设备红外热特征数字化方法

赵天成; 罗吕; 杨代勇; 刘赫; 袁刚; 许志浩

多属性融合的电力设备红外热特征数字化方法

赵天成^1,,
罗吕²,
杨代勇¹,
刘赫¹,
袁刚²,
许志浩^2, ,

1.
国网吉林省电力有限公司电力科学研究院，吉林长春 130021
2.
南昌工程学院电气工程学院，江西南昌 330099

基金项目:

吉林省电力科学研究院有限公司科技项目资助 KY-GS-20-01-07

详细信息

作者简介:
赵天成（1992-），男，吉林长春人，工程师，硕士，研究方向为电力设备故障检测与诊断。E-mail: 583107503@qq.com

通讯作者:
许志浩（1988-），男，武汉人，讲师，博士，硕导，研究方向为电力设备智能检测与人工智能应用。E-mail: zhxuhi@whu.edu.cn

中图分类号: TP391.41
计量
- 文章访问数: 121
- HTML全文浏览量: 31
- PDF下载量: 26
出版历程
- 收稿日期: 2021-03-17
- 修回日期: 2021-06-06
- 刊出日期: 2021-11-19

A Multi-Attribute Fusion Method for Digitizing Infrared Thermal Characteristics of Power Equipment

1.
Electric Power Science Research Institute of State Grid Jilin Electric Power Co., LTD., Changchun 130021, China
2.
College of Electrical Engineering, Nanchang University of Engineering, Nanchang 330099, China

摘要

摘要: 本文针对电力设备红外图像诊断中热故障特征提取和数字化表达难题，提出一种多属性融合的电力设备红外热特征数字化方法。通过对电力设备热故障特性和相关诊断文件研究分析，在对图像预处理的基础上，提取图像中关键发热区域的热点温度、热点温差、发热面积、位置信息以及热点群聚现象等热属性值，构建多属性信息融合的过热性故障特征值向量，实现热故障特征数字化描述。以断路器为例对该方法进行了验证分析，结果表明，该方法对典型红外故障图谱具有良好的描述能力，可用于后续大量复杂故障样本情况下的设备热故障智能分类与诊断应用中。
- 多属性融合 /
- 特征提取 /
- 特征向量 /
- 数字化描述
Abstract: Aiming at the complex problem of thermal fault feature extraction and digital representation in the infrared image diagnosis of power equipment, a multi-attribute fusion thermal feature digitization method for power equipment is proposed in this study. The method uses heat power equipment fault features and diagnostic files related to research analysis, based on image preprocessing, to extract the images of key areas with high temperatures, heating area, location, and thermal property values, such as hot clustering, building a multiple-attribute information fusion of overheating fault feature vectors to realize a digital description of the thermal fault characteristics. A circuit breaker is used as an example to verify and analyze the proposed method. The results show that the proposed method can effectively describe the typical infrared fault spectrum, and can be used in the intelligent classification and diagnosis of equipment faults in the case of a large number of complex fault samples.
- multi-attribute fusion /
- feature extraction /
- eigen vector /
- digital description

HTML全文

图 1 多属性信息融合特征算法流程图

Figure 1. Flow chart of multi-attribute information fusion feature algorithm

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图 2 设备局部呈现出的发热故障光斑

Figure 2. Locally the equipment presents a hot fault spot

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图 3 故障区域分割及二值化

Figure 3. Fault area segmentation and binarization

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图 4 分割出单相设备

Figure 4. A single - phase device is segmented

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图 5 形态学处理及基准建立

Figure 5. Morphological processing and datum establishment

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图 6 分割故障区域及查找等效热源点

Figure 6. Segment the fault area and find the equivalent heat source

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图 7 位置信息示意图

Figure 7. Location information schematic diagram

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图 8 像素统计图谱

Figure 8. Non-zero grayscale statistical map

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图 9 对图 4电力设备区域划分效果

Figure 9. The regional division effect of power equipment in fig.4

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图 10 热点温度与发热面积关系图

Figure 10. Diagram of hot spot temperature

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表 1 各区域的T₁与T₂对比表

Table 1 Comparison table of T₁ and T₂ for each region

		Value of simulation				Measured value
Phase sequence	Area	g	T₁/℃	T₃/℃	T₂/℃	g	T₁/℃	T₃/℃	T₂/℃
a	Superior	53	3.8	2.6	1.2	53	3.8	2.6	1.2
	Centre	70	8.5		5.9	70	8.4		5.8
	Below	48	2.6		0	48	2.6		0
b	Superior	61	6.1	3.8	2.3	62	6.2	3.7	2.5
	Centre	213	46.6		42.8	213	46.5		42.8
	Below	53	3.8		0	52	3.7		0
c	Superior	68	8.0	3.5	4.5	68	7.9	3.5	3.4
	Centre	255	57.8		54.3	255	57.8		54.3
	Below	51	3.5		0	51	3.5		0

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表 2 断路器故障等级判断标准

Table 2 Criteria for fault grade judgment of circuit breaker

Failure level	Common defect	Serious defects	Critical defect
Failure criterion/℃	0＜T＜55	55≤T≤80	T＞80

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表 3 部分电力设备热故障特征提取结果

Table 3 Thermal fault feature extraction results of powerequipment

Amount	T₁	T₂	S_i	L	M
1	48.5	45	0.008	[1.3,80,38]	2
1	42.8	38.3	0.001	[1.3,38.2,21.14]	2
2	50.6	28.3	0.009	[0.14,75,24.67]	1
⋮	⋮	⋮	⋮	⋮	⋮
149	46.5	42.8	0.009	[1.4,-47,51]	2
150	57.8	54.3	0.01	[1.3,-63,58]	2

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