首页|基于时频谱图的配电网高阻接地故障智能识别方法

基于时频谱图的配电网高阻接地故障智能识别方法

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针对配电网中发生高阻接地故障(high impedance fault,HIF)进行特征提取时,人为构造的特征量或特征向量难以充分体现高阻接地故障区别于其他事件的特征问题,提出一种波形隐含特征提取方法.通过连续小波变换将零序电流转换为时频谱图,并分割为正方形切片,然后使用卷积神经网络辨识高阻接地故障特有的"齿"形特征.结果表明:文中所提方法能由波形中的高频分量得到可视化的时频谱图,省去了人为构造的特征量的步骤,避免了特征量难以准确表征HIF特征的问题,其可靠性在仿真及现场样本测试中得到了验证.
Intelligent high impedance fault detection method for distribution networks based on time-frequency diagram
When a high impedance fault occurs in a distribution network,traditional protection means cannot be effective due to the small fault current.When emerging detection methods perform feature extraction,artificially constructed feature quantities or feature vectors often have difficulty in fully representing the characteristics that distinguish high impedance fault from other events.To this end,this paper first converts zero-sequence currents into time-frequency diagrams using continuous wavelet transform(CWT),and then segments the images into square slices.A convolutional neural network(CNN)is used to identify the characteristic"tooth"feature of high impedance fault.The proposed method is able to visualize high-frequency components of the waveform by means of the time-frequency diagram,and its reliability has been verified in simulations and field sample tests.

distribution networkhigh impedance faulttime-frequency diagramimage segmentationdeep learningconvolutional neural network

庄文睿、郭谋发

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福州大学电气工程与自动化学院,福建 福州 350108

智能配电网装备福建省高校工程研究中心,福建 福州 350108

配电网 高阻接地故障 时频谱图 图像分割 深度学习 卷积神经网络

福建省自然科学基金资助项目

2021J01633

2024

福州大学学报(自然科学版)
福州大学

福州大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.35
ISSN:1000-2243
年,卷(期):2024.52(1)
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