In an underdetermined system,compressive sensing can be used to recover the support vector.Greedy algorithms will recover the support vector indices in an iterative manner.Generalized Orthogonal Matching Pursuit(GOMP)...In an underdetermined system,compressive sensing can be used to recover the support vector.Greedy algorithms will recover the support vector indices in an iterative manner.Generalized Orthogonal Matching Pursuit(GOMP)is the generalized form of the Orthogonal Matching Pursuit(OMP)algorithm where a number of indices selected per iteration will be greater than or equal to 1.To recover the support vector of unknown signal‘x’from the compressed measurements,the restricted isometric property should be satisfied as a sufficient condition.Finding the restricted isometric constant is a non-deterministic polynomial-time hardness problem due to that the coherence of the sensing matrix can be used to derive the sufficient condition for support recovery.In this paper a sufficient condition based on the coherence parameter to recover the support vector indices of an unknown sparse signal‘x’using GOMP has been derived.The derived sufficient condition will recover support vectors of P-sparse signal within‘P’iterations.The recovery guarantee for GOMP is less restrictive,and applies to OMP when the number of selection elements equals one.Simulation shows the superior performance of the GOMP algorithm compared with other greedy algorithms.展开更多
配电网参数受天气条件和负载条件等因素影响会发生变化。由于传感装置安装有限、数据延时传输等因素,无法实时获得配电网准确参数,进而给传统故障定位方法的精度带来影响。针对以上问题,通过建立配电网数字孪生模型,基于配电网数字孪生...配电网参数受天气条件和负载条件等因素影响会发生变化。由于传感装置安装有限、数据延时传输等因素,无法实时获得配电网准确参数,进而给传统故障定位方法的精度带来影响。针对以上问题,通过建立配电网数字孪生模型,基于配电网数字孪生模型的参数自修正技术,提出了一种定位模型随参数变化动态校正的配电网故障定位方法。同时,搭建了基于数字孪生服务器和实时数字仿真系统(real time digital system, RTDS)的数字孪生平台,实现了配电网实时的物理模型和数字孪生模型的同步运行。在算例仿真中,利用该数字孪生平台,验证了基于数字孪生技术的配电网故障定法方法。结果表明,该方法可在各类系统运行条件下实时修正配电网参数,显著提高配电网故障定位的速度和精度。展开更多
提出一种压缩感知正交匹配追踪（CS-OMP）超谐波测量新算法,即运用压缩感知理论,通过引入插值系数,基于离散傅里叶变换（DFT）系数向量和狄利克雷核矩阵,构建了高频率分辨率的压缩感知模型,并基于正交匹配追踪算法,在不增加被测数据观...提出一种压缩感知正交匹配追踪（CS-OMP）超谐波测量新算法,即运用压缩感知理论,通过引入插值系数,基于离散傅里叶变换（DFT）系数向量和狄利克雷核矩阵,构建了高频率分辨率的压缩感知模型,并基于正交匹配追踪算法,在不增加被测数据观测时间前提下,将超谐波测量的频率分辨率提高了一个数量级。数值仿真分析以及两种非线性负荷的实测数据验证的结果表明,该算法可将测得数据频率分辨率由2 k Hz细化为200 Hz,能实现对被测信号中超谐波频率成分的精确定位,也可准确求解出其幅值信息,从而有效地弥补了DFT算法存在的观测时间与频率分辨率互相限制的固有缺陷,在更准确测量超谐波方面展现出良好前景。展开更多
文摘In an underdetermined system,compressive sensing can be used to recover the support vector.Greedy algorithms will recover the support vector indices in an iterative manner.Generalized Orthogonal Matching Pursuit(GOMP)is the generalized form of the Orthogonal Matching Pursuit(OMP)algorithm where a number of indices selected per iteration will be greater than or equal to 1.To recover the support vector of unknown signal‘x’from the compressed measurements,the restricted isometric property should be satisfied as a sufficient condition.Finding the restricted isometric constant is a non-deterministic polynomial-time hardness problem due to that the coherence of the sensing matrix can be used to derive the sufficient condition for support recovery.In this paper a sufficient condition based on the coherence parameter to recover the support vector indices of an unknown sparse signal‘x’using GOMP has been derived.The derived sufficient condition will recover support vectors of P-sparse signal within‘P’iterations.The recovery guarantee for GOMP is less restrictive,and applies to OMP when the number of selection elements equals one.Simulation shows the superior performance of the GOMP algorithm compared with other greedy algorithms.
文摘配电网参数受天气条件和负载条件等因素影响会发生变化。由于传感装置安装有限、数据延时传输等因素,无法实时获得配电网准确参数,进而给传统故障定位方法的精度带来影响。针对以上问题,通过建立配电网数字孪生模型,基于配电网数字孪生模型的参数自修正技术,提出了一种定位模型随参数变化动态校正的配电网故障定位方法。同时,搭建了基于数字孪生服务器和实时数字仿真系统(real time digital system, RTDS)的数字孪生平台,实现了配电网实时的物理模型和数字孪生模型的同步运行。在算例仿真中,利用该数字孪生平台,验证了基于数字孪生技术的配电网故障定法方法。结果表明,该方法可在各类系统运行条件下实时修正配电网参数,显著提高配电网故障定位的速度和精度。
文摘提出一种压缩感知正交匹配追踪（CS-OMP）超谐波测量新算法,即运用压缩感知理论,通过引入插值系数,基于离散傅里叶变换（DFT）系数向量和狄利克雷核矩阵,构建了高频率分辨率的压缩感知模型,并基于正交匹配追踪算法,在不增加被测数据观测时间前提下,将超谐波测量的频率分辨率提高了一个数量级。数值仿真分析以及两种非线性负荷的实测数据验证的结果表明,该算法可将测得数据频率分辨率由2 k Hz细化为200 Hz,能实现对被测信号中超谐波频率成分的精确定位,也可准确求解出其幅值信息,从而有效地弥补了DFT算法存在的观测时间与频率分辨率互相限制的固有缺陷,在更准确测量超谐波方面展现出良好前景。
