基于多重渐消因子变分贝叶斯的陀螺阵列融合算法

针对由多个MEMS陀螺仪组成的阵列系统在动态情况下噪声时变导致输出精度低的问题，提出了新的动态滤波模型和滤波方法。通过分析MEMS陀螺的误差特性和对角速度进行动态建模，构建了基于角速度估计的阵列陀螺随机误差动态滤波模型。由于动态情况下模型的不确定性导致传统方法精度较差，设计了一种多重渐消因子变分贝叶斯自适应卡尔曼滤波算法，利用变分贝叶斯思想和强跟踪理论提高了滤波器量测噪声估计精度、收敛速度和鲁棒性。最后在高精度转台上进行了静态实验和动态实验。实验结果表明：在静态条件下，“虚拟陀螺”方差降低为单个陀螺的4%，零偏不稳定性降低为47.2%；在动态条件下，“虚拟陀螺”能有效跟踪角速度的变化且角速度残差方差降低为单个陀螺的6.2%。该滤波算法能有效提高MEMS陀螺阵列系统的输出精度。     

基于改进鲁棒自适应UKF的配电网动态状态估计方法

配电网动态状态估计中状态方程的过程噪声统计参数是未知而且时变的,因此在状态估计过程中需要在线对过程噪声统计参数进行实时估计,而且不准确的噪声参数将会导致无迹卡尔曼滤波器的滤波性能下降甚至滤波发散。文中研究了基于改进鲁棒自适应无迹卡尔曼滤波器的配电网动态状态估计方法,其噪声参数统计估值器由一个有偏的和一个无偏的估值器组成,可以提高在状态估计过程中噪声参数估计的准确性,同时确保过程噪声方差矩阵的半正定性,从而保证算法的鲁棒性。通过对IEEE 33节点系统进行仿真验证,结果表明所提方法在系统平稳运行、负荷发生剧烈变动或者初始噪声参数值设置不当的情况下,均能保证较高的状态估计精度。     

光纤电流互感器噪声特征及建模方法研究

针对光纤电流互感器（FOCT）的随机噪声问题,借鉴光纤陀螺（FOG）的性能分析方法,分析FOCT的结构及原理,系统地探讨FOCT随机噪声的种类、噪声误差因素及噪声误差抑制措施。结合FOCT噪声所体现出的时域相干性及频域特征,引入Allan方差分析法及建模理论,辨识并量化FOCT的各项噪声误差,全面地评价FOCT性能。Allan方差分析法及建模理论在FOCT数据处理中的应用结果表明,FOCT输出数据中体现了角度随机游走、偏值不稳定性等不同特征的随机噪声,同时证明了Allan方差噪声建模理论在FOCT噪声特征分析方面的可行性。     

光纤电流互感器噪声特征及建模方法研究

针对光纤电流互感器(FOCT)的随机噪声问题,借鉴光纤陀螺(FOG)的性能分析方法,分析FOCT的结构及原理,系统地探讨FOCT随机噪声的种类、噪声误差因素及噪声误差抑制措施.结合FOCT噪声所体现出的时域相干性及频域特征,引入Allan方差分析法及建模理论,辨识并量化FOCT的各项噪声误差,全面地评价FOCT性能.Allan方差分析法及建模理论在FOCT数据处理中的应用结果表明,FOCT输出数据中体现了角度随机游走、偏值不稳定性等不同特征的随机噪声,同时证明了Allan方差噪声建模理论在FOCT噪声特征分析方面的可行性.     

基于神经网络和MS-AUKF算法的蓄电池荷电状态估计

蓄电池的荷电状态(state of charge,SOC)是表征电池当前剩余电量的重要参数。提出一种基于神经网络和主从式自适应无迹卡尔曼滤波(masterslaveadaptiveunscented Kalmanfilter,MS-UKF)算法的SOC估计方法。首先,建立蓄电池的戴维南(Thevenin)二阶模型,针对开路电压与电池SOC之间的非线性关系,采用神经网络模型代替多项式模型,以提高拟合精度。根据实时测量数据,基于最小二乘法在线确定电池模型的参数。针对传统的扩展卡尔曼滤波(extendedKalmanfilter,EKF)和无迹卡尔曼滤波(unscented Kalman filter,UKF)方法存在噪声方差固定,会产生误差造成估计精度不高的问题,采用MS-AUKF算法。该算法的主滤波器用来估计系统状态,辅助滤波器用来估计噪声方差矩阵。算法每次迭代时更新系统模型的噪声方差,克服了传统卡尔曼滤波算法中,噪声方差初值人为设定可能导致滤波发散的缺点。仿真结果表明,相比于EKF、UKF算法,MSAUKF在估计电池SOC时具有更高的精确度和收敛速度。     

基于PnP和自适应线性卡尔曼滤波的实时位姿估计

针对实时位姿估计中扩展卡尔曼滤波(EKF)线性化引入非线性误差和依赖已知噪声分布的缺点,提出一种基于Pn P的自适应线性卡尔曼滤波位姿估计求解方法。将Pn P位姿估计求解策略引入卡尔曼滤波观测方程,通过对动态方程误差统计参数实时估计,自适应调节卡尔曼滤波递推参数。所提算法求解精度高,固定了观测方程的观测向量维度,提高了算法实用性。通过仿真试验,比较了该算法与EKF的位姿估计精度,通过量化误差分析,证明了该方法可以提高三维运动位姿估计精度,也验证了该方法的有效性。     

观测模型误差不确定的锂电池SOC估计方法

建立的锂电池非线性系统中存在不确定的观测模型误差时,会影响滤波器估计的精度和稳定性,严重时还会导致估计结果发散。针对这一问题,基于变分贝叶斯自适应滤波方法,提出了一种鲁棒UKF算法。该算法构建虚拟观测噪声用来补偿观测模型误差,并采用逆Wishart分布对虚拟观测噪声协方差建模。在变分迭代过程中,实现对系统状态和虚拟观测噪声协方差的联合后验概率估计,使估计结果自适应地逼近到真实分布。利用无迹卡尔曼滤波对系统状态进行更新。结合锰酸钾锂电池非线性模型进行仿真实验表明,该算法估计锂电池荷电状态具有很好的精度、跟踪速度以及鲁棒性。     

基于模糊卡尔曼滤波算法的动力电池SOC估计

为了改善传统卡尔曼滤波算法估计SOC时量测噪声的影响,提出了将传统卡尔曼滤波算法与模糊控制相结合的动力电池SOC的自适应估计方法。通过实时监控量测噪声实际方差与理论方差之间的差值,实现对量测噪声协方差矩阵的实时在线调整,提高算法在实际应用中的鲁棒性。通过基于联邦城市行驶工况(FUDS)验证混合算法的有效性。结果表明,基于模糊卡尔曼滤波算法的SOC估计最大误差仅为0.21%,高于传统卡尔曼滤波估计精度最大误差0.53%。仿真结果表明,该方法可以有效解决传统卡尔曼滤波算法估计不准和累计误差的问题。     

MEMS陀螺仪随机误差的辨识与降噪方法研究

针对微机电系统(MEMS)陀螺仪随机误差成为制约其精度和应用范围的主要因素,提出基于回归滑动平均(ARMA)模型的卡尔曼滤波估计方法。首先基于Allan方差分析结果,确定出量化噪声、角度随机游走、零偏不稳定性是MEMS陀螺随机噪声主要组成部分;然后采用时间序列分析法对MEMS陀螺仪随机噪声的平稳性进行检验;最后基于随机漂移ARMA模型建立离散卡尔曼滤波方程对其开展误差估计与补偿。开展车载静、动态环境下的数字降噪与卡尔曼滤波估计补偿对比实验,结果表明基于ARMA模型的卡尔曼滤波估计法在MEMS陀螺随机误差补偿效果上具有更明显优势。     

多MEMS陀螺数据融合方法性能比较

MEMS陀螺的体积小、成本低,便于集成,但其低精度极大的限制了MEMS陀螺在实际中的应用。利用多传感器融合技术进行误差补偿可提高MEMS陀螺的测量精度,人们提出了多种数据融合方法用于改进MEMS陀螺的测量精度。对多尺度融合方法、卡尔曼滤波融合和小波阈值融合方法进行比较分析。理论分析与实验结果表明,多尺度融合算法相比卡尔曼滤波融合和小波阈值融合方法在标准差、信噪比、功率谱及Allan方差等方面性能获得了较好的效果,其适用范围更宽。     

Optimal configuration analysis for acampus microgrid—a case study

The foremost issues of 21st century are challenging demand of electrical energy and to control the emission of Green House Gases (GHG) emissions. Renewable energy resources based sustainable microgrid emerges as one of the best feasible solution for future energy demand while considering zero carbon emission, fossil fuel independency, and enhanced reliability. In this paper, optimization and implementation of institutional based sustainable microgrid are discussed based on cost analysis, carbon emission, and availability of energy resources. Various microgrid topologies are considered for addressing the most ideal solution. The metrological data such as irradiance is acquired from solar satellite data of NASA (National Aero Space Agency) while the data for wind speed is taken from synergy enviro engineer’s site. HOMER® simulation tool is used for modelling and optimization purpose.     

Time series modeling and filtering method of electric power load stochastic noise

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load. Modeling online and filtering real-time can effectively improve measurement accuracy. Firstly, pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load. Then, set order for the time series model by Akaike information criterion (AIC) rule and acquire model coefficients to establish ARMA (2,1) model. Next, test the applicability of the established model. Finally, Kalman filter is adopted to process the electric power load data. Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA (2,1) model. Besides, variance is reduced by two orders, and every coefficient of stochastic noise is reduced by one order. The filter method based on time series model does reduce stochastic noise of electric power load, and increase measurement accuracy     

基于时序建模的光纤电流互感器随机噪声卡尔曼滤波方法

针对光纤电流互感器(FOCT)随机噪声特性及其对继电保护、电能计量等间隔层设备的影响,建立FOCT随机误差的时序模型,并采用滤波方法有效提高了FOCT测量精确度。首先,预处理和统计检验FOCT原始数据,获取数据随机特征;根据赤池信息准则(AIC)准则选择时间序列模型的阶次,求出模型系数建立FOCT随机误差的ARMA(2,1)模型,并检验其适用性;采用卡尔曼滤波方法对FOCT输出数据进行滤波处理。总方差分析结果表明:建立的FOCT时序模型经卡尔曼滤波后,随机噪声幅值明显减小,方差值降低了两个数量级,各项随机噪声的误差系数均下降一个数量级,采用的时序建模和卡尔曼滤波方法能有效减小FOCT的随机噪声,提高电流信息的测量精确度。     

FPGA‐based embedded platform for fiber optic gyroscope signal denoising

This paper presents System on Chip (SoC) implementation of a proposed denoising algorithm for fiber optic gyroscope (FOG) signal. The SoC is developed using an Auxillary Processing Unit of the proposed algorithm and implemented in the Xilinx Virtex‐5‐FXT‐1136 field programmable gate array. SoC implementation of this application is first of its kind. The proposed algorithm namely adaptive moving average‐based dual‐mode Kalman filter (AMADMKF) is a hybrid of adaptive moving average and Kalman filter (KF) technique. The performance of the proposed AMADMKF algorithm is compared with the discrete wavelet transform and KF of different gains. Allan variance analysis, standard deviation and signal to noise ratio (SNR) are used to measure the efficiency of the algorithm. The experimental result shows that AMADMKF algorithm reduces the standard deviation or drift of the signal by an order of 100 and improves the SNR approximately by 80 dB. The Allan variance analysis result shows that this algorithm also reduces different types of random errors of the signal significantly. The proposed algorithm is found to be the best suited algorithm for denoising the FOG signal in both the static and dynamic environments. Copyright © 2013 John Wiley & Sons, Ltd.     

Degradation state analysis of the IGBT module based on apparent junction temperature

The multi-chip parallel insulated gate bipolar transistor (IGBT) is the core device in large-capacity power electronic equipment, but its operational reliability is of considerable concern to industry. The application of IGBT online degradation state analysis technology can be benefcial to the improvement of system reliability. The failure mechanism of IGBT devices is discussed in this paper, and a technique for analyzing the degradation state of IGBT based on apparent junction temperature is proposed. First, the distortion consistency of the voltage rise time in various failures is discussed, and the junction temperature dependence of the voltage rise time is then demonstrated. Subsequently, an apparent junction temperature model based on the voltage rise time is established (the ftting accuracy is as high as 94.3%). From the high-frequency model in the switching process of the device, an online extraction technology of key parameters (e.g., voltage rise time) is developed. Finally, an experimental platform for IGBT degradation state estimation is established, and the feasibility of IGBT degradation state estimation based on apparent junction temperature is proved, especially the degradation of bonding-wire and the gate-oxide-layer. The experimental results show that the proposed IGBT degradation state estimation technique based on apparent junction temperature is a reliable online estimation method with non-contact, high accuracy, and comprehensiveness.     

Degradation state analysis of the IGBT module based on apparent junction temperature

The multi-chip parallel insulated gate bipolar transistor (IGBT) is the core device in large-capacity power electronic equipment, but its operational reliability is of considerable concern to industry. The application of IGBT online degradation state analysis technology can be benefcial to the improvement of system reliability. The failure mechanism of IGBT devices is discussed in this paper, and a technique for analyzing the degradation state of IGBT based on apparent junction temperature is proposed. First, the distortion consistency of the voltage rise time in various failures is discussed, and the junction temperature dependence of the voltage rise time is then demonstrated. Subsequently, an apparent junction temperature model based on the voltage rise time is established (the ftting accuracy is as high as 94.3%). From the high-frequency model in the switching process of the device, an online extraction technology of key parameters (e.g., voltage rise time) is developed. Finally, an experimental platform for IGBT degradation state estimation is established, and the feasibility of IGBT degradation state estimation based on apparent junction temperature is proved, especially the degradation of bonding-wire and the gate-oxide-layer. The experimental results show that the proposed IGBT degradation state estimation technique based on apparent junction temperature is a reliable online estimation method with non-contact, high accuracy, and comprehensiveness.     

Process noise covariance estimation via stochastic approximation

Kalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples.