在利用双端行波法进行高压电缆局放在线检测与定位时,针对脉冲初至时刻拾取精度不高影响定位精度的问题,引入地震信号检测领域中的时变峰度法。首先利用时窗能量比检测局放事件,然后在确定的局放时窗内,通过计算时变峰度变化率并求其最...在利用双端行波法进行高压电缆局放在线检测与定位时,针对脉冲初至时刻拾取精度不高影响定位精度的问题,引入地震信号检测领域中的时变峰度法。首先利用时窗能量比检测局放事件,然后在确定的局放时窗内,通过计算时变峰度变化率并求其最大值点,初步实现脉冲初至时刻拾取。为了克服现场强噪声干扰对拾取精度的影响,利用小波包分离出局放脉冲所在的主要频带,并在此频带内求取时变峰度极大值,实现了局放脉冲初至时刻的高精度拾取。最后运用到达时间分析法实现局部放电源的在线精确定位。实验结果表明,该方法抗噪声干扰能力强,定位精度高,在-14 d B的噪声环境下定位误差小于2 m,能够满足高压电网对局放故障在线定位的精度要求,有效提高供电可靠性。展开更多
在利用多高频电流传感器进行电缆局部放电在线检测与定位时,针对局放信号初至时刻拾取精度不高影响定位精度的问题,提出一种基于AIC(Akaike’s Information Criterion)准则和时窗能量比的局放故障在线检测与精确定位方法。首先利用时窗...在利用多高频电流传感器进行电缆局部放电在线检测与定位时,针对局放信号初至时刻拾取精度不高影响定位精度的问题,提出一种基于AIC(Akaike’s Information Criterion)准则和时窗能量比的局放故障在线检测与精确定位方法。首先利用时窗能量比检测出局部放电发生的时窗,然后求取确定时窗的局部AIC特征曲线,并基于AIC准则精确拾取局放信号初至时刻。最后,运用到达时间法对局放源进行定位。仿真结果表明,该方法定位精度高,抗噪声干扰能力强,在-2 d B的噪声环境下可实现99.85%的定位准确率,具备工程实用价值。展开更多
In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accele...In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.展开更多
文摘在利用双端行波法进行高压电缆局放在线检测与定位时,针对脉冲初至时刻拾取精度不高影响定位精度的问题,引入地震信号检测领域中的时变峰度法。首先利用时窗能量比检测局放事件,然后在确定的局放时窗内,通过计算时变峰度变化率并求其最大值点,初步实现脉冲初至时刻拾取。为了克服现场强噪声干扰对拾取精度的影响,利用小波包分离出局放脉冲所在的主要频带,并在此频带内求取时变峰度极大值,实现了局放脉冲初至时刻的高精度拾取。最后运用到达时间分析法实现局部放电源的在线精确定位。实验结果表明,该方法抗噪声干扰能力强,定位精度高,在-14 d B的噪声环境下定位误差小于2 m,能够满足高压电网对局放故障在线定位的精度要求,有效提高供电可靠性。
文摘在利用多高频电流传感器进行电缆局部放电在线检测与定位时,针对局放信号初至时刻拾取精度不高影响定位精度的问题,提出一种基于AIC(Akaike’s Information Criterion)准则和时窗能量比的局放故障在线检测与精确定位方法。首先利用时窗能量比检测出局部放电发生的时窗,然后求取确定时窗的局部AIC特征曲线,并基于AIC准则精确拾取局放信号初至时刻。最后,运用到达时间法对局放源进行定位。仿真结果表明,该方法定位精度高,抗噪声干扰能力强,在-2 d B的噪声环境下可实现99.85%的定位准确率,具备工程实用价值。
基金Project(61301181) supported by the National Natural Science Foundation of China
文摘In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.
