摘要
非本征法布里-帕罗干涉(EFPI)光纤超声传感器可用于气体绝缘全封闭组合电器(GIS)内部的局部放电超声信号检测及模式识别研究,相较于传统的压电式传感器,具有灵敏度高、抗干扰能力强等优点。基于此,文中在充有0.4 MPa SF_(6)气体的GIS腔体内设置尖端、金属颗粒、悬浮和沿面4种典型的局部放电模型,创新性地利用EFPI传感器对放电超声信号进行检测,提取单次超声脉冲信号波形特征形成特征参数数据库,分别应用概率神经网络(PNN)算法和支持向量机(SVM)算法进行模式识别并比较分析。EFPI传感器检测到的超声信号特征突出,在提取特征参数的基础上,2种模式识别算法均能达到85%以上的平均识别率,且SVM的识别效果要优于PNN。
The extrinsic Fabry-Perot interferometer(EFPI)optical fiber ultrasonic sensor can be used for the detection and pattern recognition of the partial discharge ultrasonic signal inside the gas-insulated switchgear(GIS).Compared with the traditional piezoelectric sensor,it has many advantages such as high sensitivity and strong anti-interference ability.Based on this,four typical partial discharge models of tip,metal particles,suspension and quay are set in the GIS cavity filled with 0.4 MPa SF_(6) gas.The EFPI sensor is used to detect the discharge ultrasonic signal.The waveform characteristics of a single ultrasonic pulse signal are extracted to form a characteristic parameter database,and the probabilistic neural network(PNN)algorithm and the support vector machine(SVM)algorithm are respectively used for pattern recognition.The recognition results of the two algorithms are compared and analyzed.The ultrasonic signals detected by the EFPI sensor have outstanding features.Based on the extraction of feature parameters,the two pattern recognition algorithms can achieve an average recognition rate of over 85%,and the recognition rate of SVM is higher than that of PNN.
作者
韩世杰
吕泽钦
隋浩冉
王伟
屠幼萍
高超飞
HAN Shijie;LYU Zeqin;SUI Haoran;WANG Wei;TU Youping;GAO Chaofei(Beijing Key Laboratory of High Voltage&EMC(North China Electric Power University),Beijing 102206,China;State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Beijing 102206,China;State Grid Ningbo Power Supply Company of Zhejiang Electric Power Co.,Ltd.,Ningbo 315000,China;School of Automation,Beijing Information&Technology University,Beijing 100192,China)
出处
《电力工程技术》
北大核心
2022年第1期149-155,共7页
Electric Power Engineering Technology
基金
国家自然科学基金资助项目(51877080)。
