保护用电流互感器磁特性测试仪的研制

研制的测试仪主要用于测量互感器的二次侧时间常数和剩磁系数。该测试仪采用直流饱和法测量原理,是以工业控制计算机为核心,包括传感器和数据采集卡的通用测试平台。采用LabWindows/CVI编写了系统的主程序、测试界面和各功能模块程序。实验结果表明,该测试仪能够满足工业生产的需要。     

电流互感器在计算机系统中的应用

电流互感器是根据变压器的）原理制成的，它主要用来扩大测量交流电流的量程。因为要测量交流电路中的大电流时（如测量大容量交流电动机定于绕组中的电流），一般电流表的量程是达不到的。利用电流互感器就可以扩大电流表的量程。电流互感器的原理图如图1所示。其原边绕组匝数     

电流互感器二次回路电流信号自动化校验技术

为了能够避免电力设备出现故障,减少经济损失,该文提出了电流互感器二次回路电流信号自动化校验技术。根据电磁感应原理计算二次回路电流,得知磁滞回线会引发能量损失,导致饱和铁心温度升高。采用新相位误差调整电流互感器模数转换及数据处理带来的延迟,通过取样技术将基准系统数字化,修正整体的测试体系。测量电流互感器二次回路负荷和总电阻,计算电缆最大容许长度,判断是否达到制定标准,实现回路电流信号检验。通过实验,证明所提技术下电流信号准确,能够达到设定标准,以确保设备运行安全。     

浅谈电流互感器的应用

电力系统中,不能直接使用电气仪表测量大电流.所以,只能使用电流互感器,把大电流转变成小电流.本文就电流互感器的极性、运行、特点、应用等进行分析探讨.     

对称温度补偿在光电混合式电流互感器中的应用

本文介绍分析了一种光电混合型电流互感器中高电位侧传感头的温度特性,提出了对称温度补偿的原理和用软件设计的主要思路。这种对称温度补偿方法提高了测量粗度,具有很大的实用性。     

PCB电流互感器在继电保护中的应用

提出了一种以PCB电流互感器为基础的新型微机线路保护装置的设计方案,分析了PCB电流互感器的结构原理及特点,在此基础上设计了数据采集接口,整套系统采用双CPU结构,同时配合CPLD实现接口电路的扩展、时序控制以及地址译码.对装置的组成结构及各个模块进行了分析,并介绍了微机保护装置软件编程的主要内容及流程.采用PCB电流互感器的保护装置测量线性度广,采样精度高,采用一种传感元件可同时实现测量与保护的数据采集,克服了传统保护装置中电流变换器体积大、易饱和的缺点,可完全实现自动化生产,具有重要的使用价值和现实意义.     

电流互感器饱和对母线保护的影响

电流互感器饱和的问题一直是多年来影响母线保护是否属正确动作的关键问题,本文主要针对TA饱和问题进行了深入的研究,在这个基础上总结了TA饱和的一些行为特征,着重说明了低短路电流水平之下的TA饱和以及对母线保护的影响。     

门架式机械手在低压电流互感器自动检定系统中的应用

介绍了一种已在低压电流互感器自动检定系统中成功应用的门架式机械手。该机械手采用PL C作为控制系统,利用伺服电机控制器和气动电磁阀分别控制机械手运动电缸和重载型平行气爪,实现了机械手直角坐标运动、腕部电缸旋转和气爪的张开和闭合,能够完成互感器抓取和搬运的动作。现场应用证明其可靠性高,运行平稳精准,能够满足低压电流互感器自动检定系统的使用要求。     

光学电流互感器的问题与解决对策

介绍了2种光学电流互感器影响其性能的主要因素及解决对策,指出了光学电流互感器发展中需解决的共性问题,并展望了光学电流互感器的发展前景。     

电子式电流互感器相位差研究

为了提高用于小电流测量的电子式电流互感器的精度,必须对传感头进行优化,主要解决传感头的相位补偿问题。针对用于小电流测量的电子式电流互感器的特点,对传感头部分所产生相位差进行研究,根据研究结果设计了一种以全通滤波器为基础的相位补偿电路,并给出仿真结果和试验结果。结果表明其精度能达到0.2级。     

卡尔曼滤波在应变式力传感器中的应用

由于应变式力传感器系统中存在较大的随机噪声,降低了系统的标定精度和测量准确性,而卡尔曼滤波适合实时滤除干扰信号。在建立传感器测试模型基础上,通过推导卡尔曼滤波算法,确定了滤波初值和滤波参数。在传感器—A/D转换器—DSP硬件平台上,进行了滤波算法验证。实验表明：卡尔曼滤波有效地滤除了随机干扰信号,适用于静动态测量过程,提高了系统标定精度。     

Application of Kalman filtering to demographic models

In this paper demographic systems appear as stochastic and unstable linear systems. Their state representation model is simulated and identified using Kalman filtering. The example of the French beef cattle herd is presented and results are discussed with emphasis on their agroeconomic meaning.     

Unscented Kalman滤波的流量测量研究

将UKF滤波用于超声波流量测量,使用UKF滤波算法来处理超声波回波信号,得到回波信号的包络线,并且将包络模型的参数作为UKF处理的状态向量。根据流量测量的特点改进了UKF滤波运算过程,给出了UKF迭代开始和结束的条件。最后在Matlab上仿真UKF的性能及收敛速度,证明UKF是有效的和容易实现的。     

非线性离散系统的相关观测融合时变Kalman滤波

针对带相关观测噪声和带不同观测函数的多传感器离散非线性系统,利用推广的离散Kalman滤波方法对状态系统和观测系统进行线性化处理,提出了基于岭估计的加权最小二乘(REWLS)分布式融合Kalman滤波算法.以风险函数为评价指标,利用信息滤波器比较了各种观测融合Kalman滤波算法,其中REWLS分布式融合算法精度最高.同时,分布式融合算法减少了计算负担,便于实时应用.仿真例子表明了理论分析的正确性.     

IEKF滤波在移动机器人定位中的应用

针对EKF中观测噪声方差估计不准确导致滤波器性能下降甚至发散的问题,提出了基于环境特征的迭代扩展卡尔曼滤波(IEKF)融合算法。该算法融合了里程计采集的机器人内部数据和激光雷达传感器采集的外部环境特征,在测量更新阶段多次迭代状态估计值并对机器人的位姿进行修正,减少了非线性误差,提高了定位精度。     

Application of Kalman filtering to spacecraft range residual prediction

One function of the Deep Space Network is validation of the range data that they receive. In this short paper we present an automated online sequential range predictor which shows promise of significantly reducing computational and manpower expenditures. The proposed algorithm, aU-Dcovariance factored Kalman filter, is demonstrated by processing a four month record of Viking spacecraft data taken enroute to Mars.     

Continuous-time norm-constrained Kalman filtering

This paper considers continuous-time state estimation when part of the state estimate or the entire state estimate is norm-constrained. In the former case continuous-time state estimation is considered by posing a constrained optimization problem. The optimization problem can be broken up into two separate optimization problems, one which solves for the optimal observer gain associated with the unconstrained state estimates, while the other solves for the optimal observer gain associated with the constrained state estimates. The optimal constrained state estimate is found by projecting the time derivative of an unconstrained estimate onto the tangent space associated with the norm constraint. The special case where the entire state estimate is norm-constrained is briefly discussed. The utility of the filtering results developed are highlighted through a spacecraft attitude estimation example. Numerical simulation results are included.     

Multiscale fuzzy Kalman filtering

Measured data are usually contaminated with errors which sometimes mask their important features. Therefore, data filtering is needed for effective utilization of such measurements. For nonlinear systems which can be described by a Takagi–Sugeno (TS) fuzzy model, several fuzzy Kalman (FK) filtering algorithms have been developed to extend Kalman filtering to such systems. Also, multiscale representation of data is a powerful data analysis tool, which has been successfully used to solve several data filtering problems. In this paper, a multiscale fuzzy Kalman (MSFK) filtering algorithm, in which multiscale representation is utilized to improve the performance of fuzzy Kalman filtering, is developed. The idea is to apply FK filtering at multiple scales to combine the advantages of the FK filter with those of the low pass filters used in multiscale data representation. Starting with a fuzzy model in the time domain, a similar fuzzy model is derived at each scale using the scaled signal approximation of the data obtained by stationary wavelet transform (SWT). These multiscale fuzzy models are then used in FK filtering, and the FK filter with the least cross validation mean square error among all scales is selected as the optimum filter. Also, theoretically, it has been shown that applying FK filtering at a coarser scale than the time domain is equivalent to using a time-averaged FK filter. Finally, the performance of the developed MSFK filtering algorithm is illustrated through a simulated example.     

On-line discontinuities identification in noisy signals: Application to Kalman filtering

The problem of fault detection in a stochastic linear time invariant system is dealt with, assuming that the fault is modelled by a jump in the noisy output. An on-line identification procedure is designed based on a multiscale analysis and on the application of suitable statistical tests. Once the time and the size of the discontinuity have been identified, the state estimate evolution of a Kalman filter is adequately compensated.