基于CNN图像识别算法的保护装置智能巡视技术
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TM77

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国网江苏省电力有限公司科技项目“基于多元数据分析的智能屏柜运维技术研究及应用”(SGJS0000DKJS2000746)


Intelligent inspection technology of protection device based on convolution neural network image recognition algorithm
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    摘要:

    继电保护装置是保障电力系统安全稳定运行的重要环节。随着变电站及继电保护装置的数量大幅增加,日常巡视工作量已经趋于饱和,无法保证每次都实现高质量、无死角的巡视,给保护装置的可靠运行带来了隐患。文中提出基于卷积神经网络(CNN)图像识别算法的保护装置智能巡视技术,借助安装在屏柜前后的摄像头,可实现保护装置的无人或少人巡视。首先,介绍保护装置智能巡视系统,并对可实现的智能巡视项目进行分析,引出可利用CNN对其进行图像识别;然后以压板状态识别为例对巡视项目所需要的训练样本集和测试样本集进行介绍,并给出巡视项目的CNN层级;再利用训练样本集对不同巡视项目的CNN进行训练;最后,对各网络进行了测试。测试结果表明,各个巡视项目的神经网络图像识别率都在96%以上,有的可以达到98%,识别效果良好。

    Abstract:

    Relay protection device is an important part to ensure the safe and stable operation of power system. With the rapid increase of the number of substations and relay protection devices,the daily inspection workload of the operation and maintenance personnel has become saturated,which can not guarantee the high quality and no dead angle inspection every time and brings hidden dangers to the reliable operation of the protection devices. In this paper,an intelligent inspection technology of protection device based on convolution neural network image recognition algorithm is proposed. With the help of the cameras installed in the front and back of the cabinet,the unmanned or few people inspection of the protection device can be realized. Firstly,the intelligent inspection system of the protection device is introduced,and the intelligent inspection items that can be realized is analyzed. The conclusion that convolution neural network can be used for image recognition is drawn. Secondly,taking the platen state recognition as an example,the training sample set and test sample set required by the inspection items are introduced,and the convolution neural network level of the inspection items is given. Then the training sample set is used to train the convolution neural network of different inspection items,and finally each network is tested. The test results show that the neural network image recognition rate of each inspection item is above 96%,even 98%,and the recognition effect is good.

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王业,崔玉,陆兆沿,田明,张广嘉.基于CNN图像识别算法的保护装置智能巡视技术[J].电力工程技术,2022,41(6):252-257

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  • 收稿日期:2022-06-12
  • 最后修改日期:2022-09-17
  • 录用日期:2021-05-25
  • 在线发布日期: 2022-11-24
  • 出版日期: 2022-11-28