电力系统管道漏磁检测异常数据高度采集仿真

摘要

Traditionally,the data acquisition method based on equal-time interval has a large amount of data redundancy.Therefore,it is difficult to accurately reflect the change of current leakage magnetic anomaly data.This article presents a method for efficiently collecting abnormal data of pipeline magnetic flux leakage detection in power system based on sparse sampling.Firstly,the double judgment method is used to judge the degree of fluctuation of disturbance signal in magnetic flux leakage detection,and the interference on the time interval adjustment of magnetic flux leakage detection data acquisition is eliminated.On the basis of the proportional relationship between data acquisition interval and data change,the time interval change scale of data acquisition is dynamically adjusted to realize the efficient acquisition.Simulation results prove that the proposed method can reduce the amount of data collection when ensuring the quality of collecting magnetic flux leakage detection of abnormal data.Compared with the data acquisition method based on uniform time interval,the proposed method has lower degree of distortion of fitting curve,which can provides effective data guarantee on detection and diagnosis of magnetic flux leakage.%所需采集的电力系统管道漏磁异常数据需要持续不断上传至检测系统上,数据量大.传统的等时间间隔数据采集方式没有考虑到所采样数据的变化,存在数据大量冗余、难以准确反映当前漏磁异常数据变化等缺点,提出基于稀疏采样的电力系统管道漏磁检测异常数据高效采集方法.采用双重判断方法,判断漏磁检测扰动信号的波动程度,剔除其对漏磁检测数据采集时间间隔调整的干扰.再依据数据采集间隔与数据变化量之间的比例关系,动态调整数据采集时间间隔变化尺度,以实现短暂、瞬时变化下漏磁检测异常数据的高效采集.实验结果表明,所提方法可在保证采集漏磁检测异常数据质量的同时减少采集量,且相比等时间间隔数据采集方法的拟合曲线失真度较小,可为管道漏磁检测与诊断等研究工作提供有效数据保障.