深度学习在绝缘子红外图像异常诊断的应用

范鹏; 冯万兴; 周自强; 赵淳; 周盛; 姚翔宇

深度学习在绝缘子红外图像异常诊断的应用

范鹏^{1, 2,},
冯万兴^{1, 2},
周自强^{1, 2},
赵淳^{1, 2},
周盛^{1, 2},
姚翔宇^{1, 2}

1.
南瑞集团（国网电力科学研究院）有限公司，江苏南京 211106
2.
国网电力科学研究院武汉南瑞有限责任公司，湖北武汉 430074

基金项目:

国网电力科学研究院有限公司科技项目 524625190054

详细信息

作者简介:
范鹏（1986-），男，硕士，高级工程师，主要从事电网智能运检、电力物联网与人工智能方面的技术研究工作。E-mail: fanpeng2@sgepri.sgcc.com.cn

中图分类号: TN219
计量
- 文章访问数: 26
- HTML全文浏览量: 10
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2020-03-08
- 修回日期: 2020-11-22
- 刊出日期: 2021-01-20

Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis

FAN Peng^{1, 2
,},
FENG Wanxing^{1, 2},
ZHOU Ziqiang^{1, 2},
ZHAO Chun^{1, 2},
ZHOU Sheng^{1, 2},
YAO Xiangyu^{1, 2}

1.
NARI Group (State Grid Electric Power Research Institute) Co., Ltd., Nanjing 211106, China
2.
Wuhan NARI Limited Liability Company, State Grid Electric Power Research Institute, Wuhan 430074, China

摘要

摘要: 绝缘子的红外图像分析一般采用图像处理的方法，易受背景环境和数据量的影响，准确率和效率均较低，本文提出一种深度学习的异常诊断方法，基于改进的Faster R-CNN方法搭建检测网络，开展不同类型的绝缘子测试。研究结果表明：相对于神经网络（Back Propagation，BP）、Faster R-CNN方法，本文方法可高效地诊断出绝缘子的异常缺陷，平均检测精度达到90.2%；单Ⅰ型和Ⅴ型绝缘子的异常诊断准确率高于双Ⅰ型绝缘子。研究结果可为输电线路绝缘子异常诊断提供一定的参考。
- 绝缘子 /
- 异常诊断 /
- 深度学习 /
- Faster R-CNN /
- 平均检测精度 /
- 红外图谱
Abstract: Because of the effects of the background environment and data volume, the accuracy and efficiency of abnormal defects in traditional infrared images of insulators are generally low. In this study, a deep-learning anomaly diagnosis method is proposed. Based on the improved faster region-based convolutional neural network (R-CNN) method, a detection network is built to test different types of insulators. Results show that compared with the back propagation neural network and faster R-CNN methods, the proposed method can diagnose abnormal defects of insulators efficiently with a mean average precision of 90.2%. In addition, the diagnostic accuracy of single type Ⅰ and type Ⅴ insulators is higher than that of double type Ⅰ insulators. The results can provide a reference for insulator defect identification in transmission lines.
- insulator /
- abnormal diagnosis /
- deep learning /
- Faster R-CNN /
- mAP /
- infrared image

HTML全文

图 1 Faster R-CNN的算法流程

Figure 1. Algorithm flow of Faster R-CNN

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图 2 正负样本判定

Figure 2. Positive and negative sample decision

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图 3 改进的Faster R-CNN结构

Figure 3. Structure of improved Faster R-CNN

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图 4 准确率-召回率关系曲线

Figure 4. Relation curves of precision and recall

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图 5 不同类型绝缘子的红外图像

Figure 5. Infrared image of different types of insulators

下载: 全尺寸图片幻灯片

表 1 软硬件配置

Table 1. Hardware and software configuration

Name	Model
Operating system	Ubuntu 16.04.1
Database	mysql 5.5.20
CPU	Intel Xeon Silver 4114T 12C
GPU	NVIDIA GTX1080Ti
Memory	32 G
Hard disk	1 T
Frame	Detectron

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表 2 样本配置信息

Table 2. Information of sample configuration

sample type	training set	verification set	test set	total
positive	500	250	750	1500
negative	500	250	125	875
total	1000	500	875	2375

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表 3 不同方法的实验结果统计

Table 3. Statistics of experimental results by different methods

Name	Precision	Recall	mAP	Time/s
BP	93.5%	90.4%	80.3%	2.3
Faster R-CNN	98.7%	95.3%	88.7%	1.2
BFEM	99.2%	97.6%	90.2%	0.9

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表 4 绝缘子异常诊断的准确率

Table 4. Accuracy of insulator anomaly diagnosis

Insulator type	Abnormal total	Detected number	Accuracy
Single Ⅰ	62	61	98.4%
Double Ⅰ	47	44	93.6%
Ⅴ	31	31	100.0%

下载: 导出CSV

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