摘要
目前变压器绕组应变监测主要分为离线检测和在线检测,由于受现场复杂电磁环境的干扰,在线检测并未得到广泛应用,离线检测虽技术较为成熟,但无法准确判断绕组应变形式。基于以上问题,文中提出了基于分布式光纤传感的变压器绕组应变检测方法,并提出了基于S变换和极限学习机(ELM)的绕组应变识别方法。首先模拟变压器运行过程中绕组可能出现的变形形式,采集相应的布里渊频移;然后通过S变换对应变信号进行时频分析,提取变换后的时频特征量作为神经网络的输入样本,采用极限学习机(ELM)进行训练识别。实验分析表明,该方法能够有效识别常见绕组变形形式,识别效果较好,准确率高。
At present,the transformer winding strain monitoring is mainly divided into off-line detection and on-linedetection.Due to the interference of the complex electromagnetic environment,on-line detection has not been widelyused.Although the off-line detection is more mature,it can not accurately judge the winding strain form.Based onthe above problems,this paper presents a strain gage strain detection method based on distributed fiber optic sens-ing,and proposes a winding strain identification method based on S transform and extreme learning machine(ELM).First,the deformation of the winding in the process of transformer operation is simulated,and the corresponding Bril-louin frequency shift is collected.Then,the time-frequency analysis of the strain signal is carried out by S-trans-form,and the transformed time-frequency feature is extracted as the input sample of the neural network.Extremelearning machine(ELM)for training identification.Experimental results show that the method can effectively identi-fy the common winding deformation form,the recognition effect is better and the accuracy is high.
作者
刘云鹏
步雅楠
贺鹏
田源
LIU Yunpeng;BU Yanan;HE Peng;TIAN Yuan(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Hebei Baoding 071003,China;Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defence,North China Electric Power University,Hebei Baoding 071003,China)
出处
《高压电器》
CAS
CSCD
北大核心
2020年第1期9-17,共9页
High Voltage Apparatus
基金
国家电网公司科技项目(524625160020)
中央高校基本科研业务费专项资金(2016XS93,2017MS102)项目资助.
关键词
应变检测
分布式光纤传感
S变换
极限学习机
模式识别
strain detection
distributed fiber optic sensing
S-transform
extreme learning machine
pattern recognition
