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作 者:李瑞 周自强[2] 刘珊 王婷 LI Rui;ZHOU Zi-qiang;LIU Shan;WANG Ting(State Grid Shanxi Electric Power Research Institute,Taiyuan Shanxi 030001,China;Southeast University,Jiangsui Nanjing 211189,China)
机构地区:[1]国网山西省电力公司电力科学研究院,山西太原030001 [2]东南大学,江苏南京211189
出 处:《计算机仿真》2024年第4期80-84,共5页Computer Simulation
基 金:国网山西省电力公司科技项目资助(52053022000C)。
摘 要:随着电力传输技术的快速发展,高压电力电缆的应用日益普遍,对高压电力电缆终端的局部放电行为进行在线监测的需求也逐步增长。为了解决当前监测算法实效性差、准确率低等问题,提出了一种基于条件生成网络的灰度判别在线监测算法。算法首先通过红外相机对电力电缆终端进行特征采集,采集的灰度照片再使用均值化的方法进行背景去噪;然后再基于k-Means算法进行局部放电部位定位,确定电缆终端是否有放电现象;最后使用条件生成网络对放电判断步骤进行优化,并采用温差判别法判断放电程度,从而提升放电监测的准确率。实验结果表明,所提算法在局部放电监测方面准确率提升了5%,提高了监测的精确度,减小了由于局部放电对电力传输的危害。With the rapid development of power transmission technology,the application of high-voltage power cables is becoming increasingly common,and the demand for online monitoring of partial discharge behavior of highvoltage power cable terminals is also growing.In order to solve the problems of poor effectiveness and low accuracy of current monitoring algorithms,this paper proposes an online gray discrimination monitoring algorithm based on condition generation network.Firstly,the infrared camera was used to collect the features of the power cable terminal,and then the gray photos were averaged to remove the background noise;Then,based on k-means algorithm,the partial discharge position was located to determine whether there was discharge at the cable terminal;Finally,the condition generation network was used to optimize the discharge judgment steps,and the temperature discrimination method was used to judge the discharge degree,so as to improve the accuracy of discharge monitoring.The experimental results show that the accuracy of the proposed algorithm in partial discharge monitoring is improved by at least 5%,which improves the monitoring accuracy and reduces the harm of partial discharge to power transmission.
分 类 号:TM726[电气工程—电力系统及自动化]
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