[1]李昊宇,李建兴,马莹,等.基于小波特征的油浸式变压器电弧光故障诊断[J].福建工程学院学报,2019,17(01):72-76.[doi:10.3969/j.issn.1672-4348.2019.01.013]
　LI Haoyu,LI Jianxing,MA Ying,et al.Fault diagnosis of arc light of oil-immersed transformer based on wavelet characteristics[J].Journal of FuJian University of Technology,2019,17(01):72-76.[doi:10.3969/j.issn.1672-4348.2019.01.013]
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基于小波特征的油浸式变压器电弧光故障诊断()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第17卷
期数:
2019年01期
页码:
72-76
栏目:
出版日期:
2019-02-25

文章信息/Info

Title:
Fault diagnosis of arc light of oil-immersed transformer based on wavelet characteristics
作者:
李昊宇; 李建兴; 马莹; 罗堪
福建工程学院 信息科学与工程学院
Author(s):
LI Haoyu;  LI Jianxing;  MA Ying;  LUO Kan
School of Information Science and Engineering, Fujian University of Technology
关键词:
电弧光; 小波频带能量; BP神经网络; 故障识别
Keywords:
arc light;  wavelet band energy;  BP neural network;  fault identification
分类号:
TM411
DOI:
10.3969/j.issn.1672-4348.2019.01.013
文献标志码:
A
摘要:
基于小波分解频带能量特征和BP神经网络的方法识别油浸式变压器短路故障。利用电弧光信号进行油浸式变压器短路故障诊断，对不同工况下的光信号进行多分辨率分析的四层小波分解，选择合适的重构小波系数，提取特征频带。对提取出的特征频带的小波系数作平方和归一化处理，求出每个特征频带的能量，作为特征参数输入到BP神经网络模型中进行训练和故障识别。
Abstract:
Short circuit fault detection of oil-immersed transformers was studied based on wavelet decomposition band energy characteristics and BP neural network. The short-circuit fault diagnosis of oil-immersed transformer was carried out by using arc light signals, four-layer wavelet decomposition of multi-resolution analysis of optical signals under different working conditions was performed. Appropriate reconstructed wavelet coefficients were selected to extract characteristic bands. The extracted wavelet coefficients of the characteristic frequency band were normalized by the sum of squares, obtaining the energy of each characteristic frequency band, which was input as a characteristic parameter into the BP neural network model for training and fault detection.

参考文献/References:

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更新日期/Last Update: 2019-02-25