[1]张 鑫,王 衡,卫永鹏,等.基于SSA-PNN的电力变压器故障诊断[J].工业仪表与自动化装置,2022,(01):86-90.[doi:10.19950/j.cnki.cn61-1121/th.2022.01.018]
　ZHANG Xin,WANG Heng,WEI Yongpeng,et al.Fault diagnosis of power transformer based on SSA-PNN[J].Industrial Instrumentation & Automation,2022,(01):86-90.[doi:10.19950/j.cnki.cn61-1121/th.2022.01.018]
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基于SSA-PNN的电力变压器故障诊断

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年01期
页码:
86-90
栏目:
出版日期:
2022-02-15

文章信息/Info

Title:
Fault diagnosis of power transformer based on SSA-PNN
文章编号:
1000-0682（2022）01-0000-00
作者:
张 鑫1; 王 衡2; 卫永鹏1; 王胜利1; 苏益辉2
1.国网甘肃省电力公司检修公司；
2.兰州理工大学 电气工程与信息工程学院，甘肃 兰州 730050
Author(s):
ZHANG Xin1; WANG Heng2; WEI Yongpeng1; WANG Shengli1; SU Yihui2
1. State Grid Gansu Electric Power Company maintenance company ,Gansu Lanzhou 730050 ,China;?div>2. College of Electrical and Information Engineering, Lanzhou University of Technology, Gansu Lanzhou 730050,China
关键词:
变压器; 故障诊断; 麻雀搜索算法; 概率神经网络
Keywords:
transformer;  fault diagnosis;  sparrow search algorithm;  probabilistic neural network
分类号:
TM407
DOI:
10.19950/j.cnki.cn61-1121/th.2022.01.018
文献标志码:
A
摘要:
为了提高变压器故障诊断的准确率，在改良三比值法的基础上，采用麻雀搜索算法优化概率神经网络构建一种新型变压器故障诊断网络模型，并设计相应的故障诊断方法。分析表明，与基于概率神经网络的变压器故障诊断方法相比，基于该网络模型的诊断方法提高了变压器故障识别与故障分类的准确率，在电力变压器的故障诊断中具有一定的实际工程意义。
Abstract:
In order to improve the accuracy of transformer fault diagnosis, based on the improved three ratio method, the sparrow search algorithm is used to optimize the probabilistic neural network, a new transformer fault diagnosis network model is established, and the corresponding fault diagnosis method is designed. The analysis shows that compared with the transformer fault diagnosis method based on probabilistic neural network, the diagnosis method based on the network model improves the accuracy of transformer fault identification and fault classification, and has certain engineering practical significance in the fault diagnosis of power transformer.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2021-09-04

作者简介：
张鑫（1980），硕士研究生，高级工程师，主要从事变电设备运维检修、技术监督、设备异常事故的分析及处理工作。。
更新日期/Last Update: 1900-01-01