摘要
为了实现对输电线路舞动特征参数的准确辨识,提出一种基于六轴惯性传感器的舞动信号处理算法。根据捷联惯导理论,设计了舞动信号解算框架;利用舞动三自由度模型,进行反演与姿态解算;通过建立惯性传感器加速度与陀螺仪误差模型,对确定性误差采用无定向快速标定算法进行标定补偿;采用小波阈值去噪算法对环境白噪声进行去噪,确定最佳小波基函数及阈值选取原则;采用两向不水平度测定俯仰角与横滚角来计算初始四元数进行姿态更新,实现舞动幅值和舞动频率辨识算法。在实验室环境下,利用单摆运动模拟实际舞动信号,经信号处理后,较好的实现了对舞动特征参数的辨识,解算结果舞动幅值误差最大为3.58%,频率误差最大为3.67%。
Here,to accurately identify characteristic parameters of transmission line galloping,a galloping signal processing algorithm based on 6-axis inertial sensor was proposed.Based on the theory of strapdown inertial navigation,a framework for calculating galloping signals was designed.A 3-DOF galloping model was used to do inversion and attitude calculation.By establishing inertial sensor acceleration and gyroscope error model,a non-directional fast calibration algorithm was used to do calibration and compensation for deterministic errors.Wavelet threshold denoising algorithm was used to denoise environmental white noise.The optimal wavelet basis functions and threshold choosing principle were determined.The initial quaternion was calculated by measuring pitch and roll angles with 2-direction unevenness to do attitude updating,and the algorithm to identify amplitude and frequency of galloping was realized.In lab environment,simple pendulum motion was used to simulate actual galloping signals.After signal processing,the identification of galloping characteristic parameters was better realized.The calculation results showed that the maximum galloping amplitude error is 3.58%,and the maximum frequency error is 3.67%.
作者
胡涛
申立群
雷鹏
张博
董伟锋
刘梦瑶
HU Tao;SHEN Liqun;LEI Peng;ZHANG Bo;DONG Weifeng;LIU Mengyao(School of Instrumentation Science and Engineering,Harbin Institute of Technology,Harbin 150001,China;State Grid Key Lab of Transmission Line Galloping Prevention Technology,Electric Power Research Institute,State Grid Henan Electric Power Company,Zhengzhou 450052,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2023年第19期49-57,78,共10页
Journal of Vibration and Shock
关键词
惯性传感器
输电线路舞动
信号处理
小波阈值去噪
误差处理
inertial sensor
transmission line galloping
signal processing
wavelet threshold denoising
error processing
