单、三相负荷等效计算及节能分析

现行有关电气设计参考书和实际工程对单、三相负荷的等效计算,均按住建部颁发的电气设计规范中的规定执行,但规范中并未对单相负荷的属性作具体规定,这给实际设计工作带来了一定的影响。通过理论计算和分析,提出了单相相负荷及线负荷等效为三相负荷的两种计算方法,并达到节能的目的。     

目标函数优化的有源滤波器差拍控制算法

以并联型有源滤波器为例,以降低差拍控制算法的复杂度为目的,通过优化目标函数提出了3种差拍控制算法。其中一范数差拍控制算法和无穷范数差拍控制算法通过构造新目标函数对算法进行化简,更适于控制三相有源滤波器,其新目标函数与传统差拍控制的二范数目标函数等价。通过优化传统差拍控制的目标函数得到改进二范数差拍控制算法,该算法的运算量比一范数算法和无穷范数算法少,适用于控制三相及单相有源滤波器。仿真结果表明上述3种算法均具有与传统差拍算法十分接近的控制精度,对控制周期变化的适应性相同,可完全取代传统差拍算法。考虑到算法的复杂度,改进二范数差拍控制算法最具应用价值。     

An L norm for efficient power quality monitoring

Real-time monitoring of power quality requires extensive data-handling and data-processing capabilities. These requirements limit the scope of monitoring, despite the fact that microprocessor-based monitoring systems have seen significant increase in their storage and computational power. Development of compact algorithms will benefit power quality in two ways; they will allow monitoring of more points simultaneously for large systems, and they will help create powerful embeddable monitoring architectures within small power devices, such as a breaker, motors, or power drives. This paper proposes use of the distance L¹ norm as an indicator of power quality. We show how this approach will improve the computational and storage requirements. This work presents analyses of the proposed norm, how it compares with traditional approaches, and examples of its applications.     

一种结合快速L_1范数最小化算法的柱面匀场线圈目标场设计方法

目标场方法由Turner于1986年提出,到目前为止该方法已成为核磁共振成像系统中匀场、梯度线圈设计的一种主流方法。各种改进的目标场方法纷纷涌现。为解决线圈设计中的有限尺寸问题,Forbes和Crozier提出了一种新方法,该方法预先通过三角函数来约束线圈面上的电流密度分布,为克服方程求解过程中的病态问题,对电流密度表达式中的待定系数采用最小均方差(least square,LS)和L2范数相结合的方法来进行估计,成功得到了有限长匀场线圈的设计结果。该文采用最小均方差和L1范数结合的方法来对面电流密度分解表达式中的未知系数进行估计,并将该方法应用于核磁共振成像(magnetic resonance imaging,MRI)系统的匀场线圈设计中。系统主磁体长度为3 m,室温孔直径为1.6 m,目标区域(region of interest,ROI)直径为0.4 m。算例采用Matlab仿真平台,结果表明该文提出的LS-L1目标场方法相比于传统的LS-L2目标场方法磁场偏差更小、效果更好。     

基于隶属度时段划分的配电网动态重构

为更好地实现配电网优化运行,提出一种基于隶属度时段划分的配电网动态重构方法。以配电网整个时段内综合运行费用最小为目标函数,建立配电网动态重构数学模型。将系统各时段有功网损与电压偏差之比作为系统运行指标,计算指标峰谷隶属度及变化率对时段进行划分。为增强二进制粒子群算法(BPSO)摆脱局部最优解的能力,将模拟退火算法(SA)中基于Mertropolis准则的新粒子接收机制引入BPSO中,克服二进制粒子群算法易陷入早熟收敛的局限性。算例结果验证了所提方法的有效性。     

基于Sage-Husa卡尔曼滤波的三相电压暂降检测

针对三相三线制系统,提出一种检测三相电压暂降的新方法。通过瞬时对称分量法在αβ静止坐标系下建立离散检测模型,引入90°超前移相算子,解耦了暂降电压正序、负序分量的检测;采用Sage-Husa卡尔曼滤波算法实现移相算子,产生正交分量,同时表现出算法自身的滤波功能;引入欧氏空间距离定位故障发生及恢复时刻,并重置卡尔曼滤波器参数,加快检测响应速度。仿真结果表明,所述方法在响应速度和稳态精度方面具有优越性,证明了该方法的可行性和有效性。     

Mixed norm regularized recursive total least squares for group sparse system identification

A mixed l_p,0‐regularized recursive total least squares (RTLS) algorithm is considered for group sparse system identification. Regularized recursive least squares (RLS) has been successfully applied to group sparse system identification; however, the estimation performance in regularized RLS‐based algorithms deteriorates when both input and output are contaminated by noise (the error‐in‐variables problem). We propose an l_p,0‐RTLS algorithm to handle group sparse system identification with errors‐in‐variables. The proposed algorithm is an RLS‐like solution that utilizes l_p,0‐regularization. The proposed algorithm provides excellent performance as well as reduces the required complexity by effective inversion matrix handling. Simulations demonstrate the superiority of the proposed l_p,0‐regularized RTLS for a group sparse system identification setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Group sparse RLS algorithms

Group sparsity is one of the important signal priors for regularization of inverse problems. Sparsity with group structure is encountered in numerous applications. However, despite the abundance of sparsity‐based adaptive algorithms, attempts at group sparse adaptive methods are very scarce. In this paper, we introduce novel recursive least squares (RLS) adaptive algorithms regularized via penalty functions, which promote group sparsity. We present a new analytic approximation for ?_p,0 norm to utilize it as a group sparse regularizer. Simulation results confirm the improved performance of the new group sparse algorithms over regular and sparse RLS algorithms when group sparse structure is present. Copyright © 2013 John Wiley & Sons, Ltd.     

一种稀疏孔径逆合成孔径雷达成像算法

针对逆合成孔径雷达成像在稀疏孔径条件下存在方位向分辨率低、易受噪声干扰等问题,利用目标二维分布的稀疏性将成像问题转换为多测量向量模型下稀疏信号的重构问题,采用零范数最小均方法并行处理以提高运行效率,使用最优步长公式代替固定步长以避免步长设置不当对收敛速度和性能的影响,并利用平滑零范数法逼近零范数以提高重构精度和抗噪性能。相较于已有算法,新算法能够获得更高质量的目标图像,对噪声鲁棒性强,并且计算量更低。仿真和实测数据的处理结果,验证了该算法的有效性。     

基于混合熵和L_1范数的遥感图像分类

针对遥感图像数据具有的高维数、非线性以及海量无标记样本的特性,提出了一种基于混合熵和L1范数的概率型最小二乘支持向量机分类方法.将准熵和熵差分融合,构造一种混合熵用以从海量无标记样本集中选出最有＂价值＂的待标记样本;基于L1范数距离度量,进一步从待标记样本集中筛选出孤立点和冗余点加以剔除;基于初始已标记样本以及筛选得到的样本,训练得到概率型最小二乘支持向量机.对反射光学系统的成像光谱仪（ROSIS）高光谱遥感图像进行了分类实验.结果表明：所提分类方法的总精度和Kappa系数分别达到了89.90%和0.868 5,能够以较少的训练样本得到较高的分类精度,其更适于处理遥感图像分类问题.