随着分布式电源(distributed generation,DG)的容量变化,微电网原有的供电结构发生改变,使得潮流大小、方向和功率结构发生变化,对快速检测和定位微电网中的短路故障区域提出了挑战。在MATLAB/Simulink中搭建低压交流微电网模型;通过高...随着分布式电源(distributed generation,DG)的容量变化,微电网原有的供电结构发生改变,使得潮流大小、方向和功率结构发生变化,对快速检测和定位微电网中的短路故障区域提出了挑战。在MATLAB/Simulink中搭建低压交流微电网模型;通过高尺度小波能量谱算法对微电网与大电网公共连接点(point of common coupling,PCC)处检测到的电流进行分解,提取适应不同容量情况的短路故障特征值,实现了不同容量下微电网短路故障的早期检测;利用小波能量谱特征结合基于正交最小二乘法(orthogonal least square,OLS)的径向基函数(radial basis function,RBF)神经网络算法提出一种适用于不同容量微电网的短路故障区域定位方法,并进行仿真验证;在此基础上设计并网模式微电网短路故障保护硬件系统,并进行实验验证。结果表明,所设计的保护系统能够快速、准确地同时实现并网模式下交流微电网短路故障的早期检测与区域定位。展开更多
A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning ar...A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning architecture is more robust to initial conditions of the tap coefficients than that based on in-direct learning architecture;on the other hand,by using two polynomial coefficient combinations,differ-ent polynomial coefficient combination can be selected when the input signal amplitude changes,whicheffectively decreases the estimate error.This paper introduces the direct learning architecture and givesthe dynamic coefficient polynomial expression.A simplified nonlinear recursive least-squares(RLS)algo-rithm for polynomial coefficient estimation is also derived in detail.Computer simulations show that theproposed predistorter can attain 31 dB,28dB and 40dB spectrum suppression gain when our method is ap-plied to the traveling wave tube amplifier(TWTA),solid state power amplifier(SSPA)and polynomialpower amplifier(PA)model,respectively.展开更多
In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise vector.Orthogonal least squares(OLS),which selec...In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise vector.Orthogonal least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery algorithms.In this paper,we investigate the number of iterations required for recovering x with the OLS algorithm.We show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.展开更多
We investigate numerical approximations based on polynomials that are orthogonal with respect to a weighted discrete inner product and develop an algorithm for solving time dependent differential equations.We focus on...We investigate numerical approximations based on polynomials that are orthogonal with respect to a weighted discrete inner product and develop an algorithm for solving time dependent differential equations.We focus on the family of super Gaussian weight functions and derive a criterion for the choice of parameters that provides good accuracy and stability for the time evolution of partial differential equations.Our results show that this approach circumvents the problems related to the Runge phenomenon on equally spaced nodes and provides high accuracy in space.For time stability,small corrections near the ends of the interval are computed using local polynomial interpolation.Several numerical experiments illustrate the performance of the method.展开更多
OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ...OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ,该算法使用 AF CM方法对数据进行聚类 ,并获取基函数的平滑参数 ,然后使用 OL S方法从聚类结果中选取网络中心。利用实测的 4类飞机目标数据对其进行性能检验 ,试验结果验证了该训练算法可提高网络的训练速度 ,缩小网络规模 ,提高网络的分类能力。展开更多
文摘随着分布式电源(distributed generation,DG)的容量变化,微电网原有的供电结构发生改变,使得潮流大小、方向和功率结构发生变化,对快速检测和定位微电网中的短路故障区域提出了挑战。在MATLAB/Simulink中搭建低压交流微电网模型;通过高尺度小波能量谱算法对微电网与大电网公共连接点(point of common coupling,PCC)处检测到的电流进行分解,提取适应不同容量情况的短路故障特征值,实现了不同容量下微电网短路故障的早期检测;利用小波能量谱特征结合基于正交最小二乘法(orthogonal least square,OLS)的径向基函数(radial basis function,RBF)神经网络算法提出一种适用于不同容量微电网的短路故障区域定位方法,并进行仿真验证;在此基础上设计并网模式微电网短路故障保护硬件系统,并进行实验验证。结果表明,所设计的保护系统能够快速、准确地同时实现并网模式下交流微电网短路故障的早期检测与区域定位。
基金the National High Technology Research and Development Programme of China(No2006AA01Z270)Beijing Jiaotong University Talent Foundation(No2007RC022)
文摘A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning architecture is more robust to initial conditions of the tap coefficients than that based on in-direct learning architecture;on the other hand,by using two polynomial coefficient combinations,differ-ent polynomial coefficient combination can be selected when the input signal amplitude changes,whicheffectively decreases the estimate error.This paper introduces the direct learning architecture and givesthe dynamic coefficient polynomial expression.A simplified nonlinear recursive least-squares(RLS)algo-rithm for polynomial coefficient estimation is also derived in detail.Computer simulations show that theproposed predistorter can attain 31 dB,28dB and 40dB spectrum suppression gain when our method is ap-plied to the traveling wave tube amplifier(TWTA),solid state power amplifier(SSPA)and polynomialpower amplifier(PA)model,respectively.
基金supported by the National Natural Science Foundation of China(grant nos.61907014,11871248,11701410,61901160)the Natural Science Foundation of Guangdong province(No.2021A1515010857)+2 种基金Youth Science Foundation of Henan Normal University(grant no.2019QK03)China Postdoctoral Science Foundation(grant no.2019M660557)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019).
文摘In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise vector.Orthogonal least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery algorithms.In this paper,we investigate the number of iterations required for recovering x with the OLS algorithm.We show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.
文摘We investigate numerical approximations based on polynomials that are orthogonal with respect to a weighted discrete inner product and develop an algorithm for solving time dependent differential equations.We focus on the family of super Gaussian weight functions and derive a criterion for the choice of parameters that provides good accuracy and stability for the time evolution of partial differential equations.Our results show that this approach circumvents the problems related to the Runge phenomenon on equally spaced nodes and provides high accuracy in space.For time stability,small corrections near the ends of the interval are computed using local polynomial interpolation.Several numerical experiments illustrate the performance of the method.
文摘OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ,该算法使用 AF CM方法对数据进行聚类 ,并获取基函数的平滑参数 ,然后使用 OL S方法从聚类结果中选取网络中心。利用实测的 4类飞机目标数据对其进行性能检验 ,试验结果验证了该训练算法可提高网络的训练速度 ,缩小网络规模 ,提高网络的分类能力。
