基于光纤光栅的OPGW异常振动信号分类识别  

Classification and Recognition of OPGW Abnormal Vibration Signals Based on Fiber Bragg Grating

作　　者：李峰 张建业 霍伟伟 郭庆瑞 张骞子 LI Feng;ZHANG Jian-ye;HUO Wei-wei;GUO Qing-rui;ZHANG Qian-zi(Electric Power Research Institute,State Grid Xinjiang Electric Power Co.,LTD.,Urumqi 830000,China;State Grid Xinjiang Electric Power Co.,LTD.,Urumqi 830000,China;State Grid Xinjiang Yili Yihe Power Supply Co.,LTD.,Yili 835100,China;Beijing iot Perception Technology Co.,LTD.,Beijing 101100,China)

机构地区：[1]国网新疆电力有限公司电力科学研究院,新疆乌鲁木齐830000 [2]国网新疆电力有限公司,新疆乌鲁木齐830000 [3]国网新疆伊犁伊河供电有限责任公司,新疆伊犁835100 [4]北京物联感知科技有限公司,北京101100

出　　处：《光学与光电技术》2023年第5期38-42,共5页Optics & Optoelectronic Technology

摘　　要：基于光纤光栅的OPGW异常监测得到了广泛研究,但异常检测数据在分类识别判断是否为正常信号和虚假信号上存在较大困难,针对上述问题,研究了基于改进型的网格搜索法进行随机森林分类。首先在理论上构建分类识别模型,通过粒子群算法迭代寻找最优分类解;然后将监测数据分为多帧训练集、测试集和验证集,分别实现传统随机森林算法、网格搜索随机森林算法和改进型网格搜索随机森林算法对异常振动分类识别;最后利用异常信号识别准确率和精确度具体量化三种算法对比结果。实际分类计算结果表明,所研究的改进型网格搜索随机森林算法在测试集异常信号识别准确率可达98.56%,验证集异常信号识别准确率可达99.56%,证明了方法的有效性,对OPGW光缆异常振动分类识别具有实际意义。OPGW anomaly monitoring based on fiber Bragg grating has been widely studied,but anomaly detection data is difficult to classify and identify whether it is a normal signal or a false signal.Aiming at the above problems,this paper studies random forest classification based on improved grid search method.Firstly,a classification and recognition model is built theoretically.The optimal classification solution is found iteratively by particle swarm optimization algorithm.Then,the monitoring data is divided into multi-frame training set,test set and verification set to realize classification and recognition of abnormal vibration by traditional random forest algorithm,grid search random forest algorithm and improved grid search random forest algorithm.Finally,the accuracy and precision of identifying abnormal signals are used to quantify the comparison results of the three algorithms.It is proved that the improved grid search random forest algorithm studied in this paper can achieve 98.56%accuracy of abnormal signal recognition in test set and 99.56%accuracy of abnormal signal recognition in verification set,which proves the effectiveness of the method and has practical significance for classification and recognition of OPGW optical cable abnormal vibration.

关 键 词：光纤光栅 光纤复合架空地线 随机森林 异常振动 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]