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以卷积神经网络对中压电力设备行故障识别的方案设计

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卷积神经网络是深度学习算法的一种,一般用于图像识别和处理任务,能够通过多个卷积层和池化层提取图像的特征,从而发现问题.如果将该功能用于电力设备的故障识别,用卷积层来提取电力设备图像的特征,如设备结构、损坏部位等,可以提高设备故障的识别准确率和效率.鉴于此,就卷积神经网络对中压电力设备行故障识别的方案设计进行探讨,首先分析卷积神经网络的图像识别原理,其次探究中压电力设备的常见故障类型及表现,最终提出设计措施,为相关识别机制的设计提供良好的学术参考.
Scheme Design of Convolutional Neural Network for Fault Identification of Medium Voltage Power Equipment
Convolutional Neural Network(CNN)is a type of deep learning algorithm commonly used for image recognition and pro-cessing tasks.It can extract features of images through multiple convolutional and pooling layers to identify issues.By applying this functional-ity to the fault identification of power equipment,using convolutional layers to extract features of power equipment images,such as equip-ment structure and damaged parts,can improve the accuracy and efficiency of fault identification.In view of this,this paper explores the scheme design of convolutional neural network for fault identification of medium voltage power equipment.It first analyzes the image recog-nition principles of CNN,then investigates the common fault types and manifestations of medium voltage power equipment,and finally proposes design measures to provide good academic references for related identification mechanisms.

Convolutional neural networkMedium voltage power equipmentFault identification

陈晓明、邹金芳、余文斌、倪婉滨

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国能(福州)热电有限公司,福建福州 350309

卷积神经网络 中压电力设备 故障识别

2024

机电产品开发与创新
中国机械工业联合会

机电产品开发与创新

影响因子:0.211
ISSN:1002-6673
年,卷(期):2024.37(3)