基于卷积神经网络与特高频技术的局部放电模式识别研究  

Partial Discharge Pattern Recognition Based on ConvolutionalNeural Network and UHF Technology

作　　者：孙天龙 SUN Tianlong(China Coal Technology and Engineering Group Shenyang Reseach Institute Co.,Ltd.,Fushun 113122,China;State Key Laboratory of Coal Mine Safety Technology,Fushun 113122,China)

机构地区：[1]中煤科工集团沈阳研究院有限公司,辽宁抚顺113122 [2]煤矿安全技术国家重点实验室,辽宁抚顺113122

出　　处：《电器与能效管理技术》2022年第10期32-37,共6页Electrical & Energy Management Technology

摘　　要：局部放电是导致电力设备绝缘性能劣化并最终导致绝缘失效的主要因素之一,传统方法检测设备局部放电操作复杂、需要依靠人工定期巡检,不能对设备绝缘状态进行实时监测。由于局部放电发生时会向外辐射高频电磁波信号,提出一种基于特高频技术的局部放电在线监测及相位分辨的局部放电(PRPD)图谱构建方法。使用所提方法获取局部放电脉冲的幅值及时间信息构建PRPD图谱,将PRPD图谱进行网格化处理得到36×30的灰度图像,最后采用卷积神经网络算法对4种典型放电类型图谱进行分类识别。Partial discharge is one of the main factors that lead to the deterioration of the insulation performance of power equipment and eventually lead to insulation failure.The traditional method of detecting partial discharge of equipment is complicated,requires regular manual inspection,and cannot monitor the insulation status of equipment in real time.When partial discharge occurs,high-frequency electromagnetic wave signals will be radiated outward.An online monitoring of partial discharge based on UHF technology and a method for constructing a phase resolved partial discharge(PRPD)map is proposed.This method is used to obtain the amplitude and time information of partial discharge pulses to construct a PRPD map,and the PRPD map is gridded to obtain a grayscale image of 36×30 size.Finally,the optimized convoluntional neural network model is used to identify the four typical discharge types.

关 键 词：局部放电 模式识别 卷积神经网络 PRPD图谱 

分 类 号：TM930[电气工程—电力电子与电力传动]