基于深度卷积神经网络的变电一次设备故障检测方法研究

Research on Fault Detection Method for Substation Primary Equipment Based on Deep Convolutional Neural Network

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中文摘要：该文介绍了一种变电一次设备故障检测方法:通过不同光照环境收集一次设备的图像,创建设备数据集并进行预处理,通过深度卷积神经网络提取设备特征并加以检测.经检测,此方法能够明显降低变电一次设备故障的漏报和误报率.

外文摘要：This paper designs a substation primary equipment fault detection method:collecting images of the primary equipment in different lighting environments,creating equipment datasets and preprocessing them,and extracting equipment features for detection based on deep convolutional neural networks.After testing,this method can significantly reduce the missed and false alarm rates of primary equipment faults in substations.

外文关键词：

convolutional neural Networksubstationone devicefault detection

作者：

张化凯

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作者单位：

盐城师范学院,江苏盐城 224007

关键词：

卷积神经网络变电站一次设备故障检测

出版年：

2024

DOI：