摘要
光电复合海底电缆(简称“海缆”)的在线状态监测及故障识别,可以实现海缆故障的早期预警。为了更快、更准确地实现海缆故障早期预警,文章提出一种基于麻雀搜索算法优化支持向量机的海缆振动信号识别的方法。其首先采用集合经验模态分解(EEMD)方法对海缆故障信号进行分解,并提取各个分量的峭度、能量熵组合作为训练特征集,以避免直接去噪导致信号失真而影响对目标特征的提取;然后采用麻雀搜索算法(SSA)优化支持向量机(SVM)的惩罚因子和核函数参数,以提高识别准确率。通过基于布里渊光时域分析(BOTDA)的海缆振动信号模拟实验系统获取锚砸、冲刷和摩擦3种工况下的海缆振动信号各500组,并通过EEMD对3类含噪信号进行分解,提取各分量的特征数据集,同时将数据集的80%作为训练集,其余的20%作为测试集。将文中所提EEMD-SSA-SVM算法与EEMD-PSO-SVM和SVM算法进行比较,结果显示,EEMD-SSA-SVM算法识别率高,优化能力强,其测试集准确率达到95%,优于其他几类算法。
Online status monitoring and fault recognition of photoelectric composite submarine cables(hereinafter referred to as submarine cables)offers an effective approach for early warning of faults in submarine cables.In order to improve the speed and accuracy of this practice,this paper proposes a submarine cable vibration signal recognition method based on the sparrow search algorithm(SSA)optimized support vector machine(SSA-SVM).Firstly,the submarine cable fault signal was decomposed by the ensemble empirical module decomposition(EEMD)method,allowing for extraction of the kurtosis and energy entropy combination of each component as the training feature set,so as to avoid any potential signal distortion caused by direct noise reduction that may affect the extraction of target features.Then,the SSA was used to optimize the penalty factor and kernel function parameters of the SVM,thereby improving the recognition accuracy.Finally,through the submarine cable vibration signal simulation system based on Brillouin optical time domain analysis(BOTDA),500 groups of submarine cable vibration signals under anchor smashing,erosion and friction conditions were obtained,and 3 types of noisy signals were decomposed by EEMD,to extract the feature data set of each component,in which 80%is taken as the training set,and the remaining 20%as the test set.The EEMD-SSA-SVM algorithm proposed in this paper was compared with the EEMD-PSO-SVM and SVM algorithms.The results show that the EEMD-SSA-SVM algorithm exhibits higher accuracy and superior optimization ability.Specifically,the accuracy of the test set reaches 95%,surpassing that achieved by other algorithm models.
作者
郭家兴
钱君霞
柳瑞
马鸿娟
GUO Jiaxing;QIAN Junxia;LIU Rui;MA Hongjuan(China Energy Engineering Group Jiangsu Power Design Institute Co.,Ltd.,Nanjing,Jiangsu 210000,China;North China Electric Power University(Baoding),Baoding,Hebei 071003,China)
出处
《控制与信息技术》
2023年第5期47-54,共8页
CONTROL AND INFORMATION TECHNOLOGY
基金
国家自然科学基金项目(61775057)
河北省自然科学基金项目(E2019502179)。
关键词
模式识别
海缆
麻雀搜索算法
支持向量机
信号分解
pattern recognition
submarine cable
sparrow search algorithm(SSA)
support vector machine(SVM)
signal decomposition
作者简介
郭家兴(1997—),男,硕士,主要从事智能电力系统控制与设计、模式识别与人工智能方面的研究。
