基于GaBP算法的快速潮流计算方法

研究在潮流迭代求解过程中雅可比矩阵方程组的迭代求解方法及其收敛性。首先利用PQ分解法进行潮流迭代求解,并针对求解过程中雅可比矩阵对称且对角占优的特性,对雅可比矩阵方程组采用高斯置信传播算法(GaBP)进行求解,再结合Steffensen加速迭代法以提高GaBP算法的收敛性。对IEEE118、IEEE300节点标准系统和两个波兰互联大规模电力系统进行仿真计算后结果表明：随着系统规模的增长,使用Steffensen加速迭代法进行加速的GaBP算法相对于基于不完全LU的预处理广义极小残余方法(GMRES)具有更好的收敛性,为大规模电力系统潮流计算的快速求解提供了一种新思路。     

求解非线性方程组的改进不精确雅可比牛顿法

在分析不精确雅可比牛顿法的基础上,进一步研究了不精确雅可比矩阵在精确解附近奇异的求解方法。利用雅可比矩阵与函数自身,在不增加新的计算量前提下,得到改进的求解非线性方程组的不精确雅可比牛顿算法。数值结果表明,改进后算法与原不精确雅可比牛顿法具有相同的计算效率,而且在使用上更为方便,有效。     

基于雅可比矩阵逆预处理的快速潮流计算方法

随着电网规模变大，利用稳定双共轭梯度法（Bi-CGSTAB）求解潮流计算中的修正方程组时，收敛速度会变得很慢。通过寻找合适的预处理矩阵是解决问题的关键。研究了雅可比矩阵预处理方法，针对牛顿法求解潮流过程中雅可比矩阵的变化特性，提出将第一次外迭代的雅可比矩阵逆作为预处理矩阵，并与稳定双共轭梯度法相结合，提高潮流计算的收敛速度。借助InterPSS电力系统仿真软件，对IEEE118、IEEE162、IEEE300和一个欧洲大陆真实电力系统进行仿真计算，验证了在处理大规模电网时，所提方法相对稀疏近似逆预处理具备更好的有效性。     

平行导路6-PSS并联机构运动学研究

文中提出了一种新型平行导路6-PSS结构并联机器人,介绍了这种机构的特点并对其运动学相关算法进行了深入研究,其中包括运动学反解、雅可比矩阵以及正解的求解方法,同时还与6-SPS结构并联机器人运动学相关的算法进行了比较.由于文中采用构造法计算雅可比矩阵,避免了以往对反解方程直接求导的计算方式,从而使雅可比矩阵的计算大为简化,而且也减少了正解的计算量、精度得到提高.实验结果证明,这些算法正确、实用.     

用计算机求取雅可比矩阵

一、前言在化工冶金的计算中,许多行之有效的数学方法,如解非线性方程(组)的牛顿法等,需使用导函数矩阵,即雅可比矩阵。在以往的工作中,由于推导烦琐、易错等原因,不少计算工作者都用中心差分来避开它。但这种方法对初值要求苛刻,有时有收敛性差的弊病,使得某些非常好的算法的使用受到限制。所以怎样利用计算机代替部分的人工智能,获取正确的雅可比矩阵是一个具有吸引力的问题。我们在国产TQ—16机上进行了初步的尝试,取得了一定的进展。     

自由漂浮空间机器人回避动力学奇异的轨迹规划

针对非完整约束导致的自由漂浮空间机器人广义雅可比矩阵动力学奇异性问题,提出一种间接求解机器人逆运动学的方案.该方案通过运动方程分解并构造迭代函数,将动力学奇异转换为运动学奇异问题,避免直接求解逆广义雅可比矩阵,解决广义雅可比矩阵动力学参数相关特性问题,仿真结果表明该方法能够有效地实现自由漂浮空间机器人回避动力学奇异的非完整轨迹规划.     

双足机器人逆运动学的模糊自适应算法

针对双足机器人逆运动学的数值解法中存在的雅可比矩阵奇异性和调节参数固定问题,提出了一种改 进的求解方法．运用微分运动方程的近似解避开雅可比矩阵求逆,利用能够减小跟踪误差的自适应模糊控制法,调 节自适应参数以使近似解任意逼近精确解,从而得到了精确性极高和强鲁棒性的模糊自适应算法．通过双足机器人 运动学的仿真分析,验证了该算法的有效性．而且整套算法的计算时间约为0.35 ms,可以用于实际双足机器人的实 时控制．     

拟牛顿法求解化工过程数学模型

使用不需求取偏导数的拟牛顿法,求解化工过程模拟中产生的非线性方程组形式的数学模型。当未知数各分量间绝对值相差较大时,提出了改善收敛性的几种方法,即：（1）加入阻尼因子,以减少迭代值的震荡、（2）将方程适当降价;（3）将差商的绝对步长改为相对步长;（4）新迭代值超来其物理意义范围时,强制其回至其初始值。计算结果表明,与牛顿－拉夫森法相比,拟牛顿法不需求偏导数,对初值要求低,较雅可比迭代法收敛速度快,可用于求解化工过程的非线性方程组。     

二阶中心差分粒子滤波FastSLAM算法

为改善SLAM算法中非线性系统状态估计精度不高,计算繁杂的问题,本文创新性地提出了基于二阶中心差分滤波并融合最新观测数据来产生建议分布函数的新算法。新算法基于二阶sterling插值公式处理SLAM中的非线性系统问题,无须计算雅可比矩阵,容易实现。此外,该算法使用Cholesky分解技术,在SLAM概率估计中直接依据协方差平方根因子进行传播,保证协方差矩阵正定性的同时减小了局部线性化的截断误差。仿真试验表明,在粒子数相同的情况下,二阶中心差分FastSLAM(SOFastSLAM)在不同噪声条件下的估计精度均优于FastSLAM2.0、UFastSLAM算法,且用时最少,证实了SOFastSLAM算法的优越性。     

基于RLS的改进型盲多用户检测算法

针对传统的关于可变约束MOE盲多用户检测算法需直接进行特征值分解计算和不能实现二次约束限制的缺点,提出了一种基于RLS的改进型MOE盲多用户检测算法.该算法避免了矩阵求逆和大量的特征值分解运算,计算复杂度大大降低.仿真结果表明,该算法的误码率性能和收敛性均优于直接利用特征值分解的传统算法,且程序运行时间大大缩小.     

一种实用的高压配电网不良数据处理方法

为满足高压配电网不良数据处理实用化的需求,提出一种针对高压配网不良数据处理的实用方法。该方法充分考虑高压配网的可观测性与辐射性,采用数据挖掘技术对坏数据进行检测与辨识,并综合利用前推回代潮流法与改进遗传算法获得加权估计值。该方法已在某市的调度系统中得到实际应用,表明了其有效性。     

最优潮流问题的解耦半光滑牛顿型算法

提出一种求解最优潮流（OPF）问题的新算法--解耦半光滑牛顿型算法.该算法是对作者的投影半光滑Newton算法的改进和提高,它除了保持原算法不必识别不等式约束、对界约束的特殊处理以减少讨论问题的维数等优点外,其显著的特点是结合了电力系统固有的弱耦合性质,构造了求解OPF问题的一类解耦半光滑牛顿算法.解耦算法可达到加快计算速度、提高计算效率的目的.IEEE多个算例的数值实验以及与其他方法的比较均显示了新算法具有良好的计算效果.     

配电网无功优化软件的设计

关于优化配电网的质量问题,针对目前配电网无功补偿普遍缺乏合理的无功规划,导致运行电压较低及较大的线损的问题,迫切要求改善现有配电网无功优化的现状,为有效地提高现有无功补偿装置的效率,提升配电网运行电压以及降低线损。结合配电网绝大部分线路是放射状的实际情况,以辐射型网络潮流的分层前推回代算法分析计算配电网的潮流,在潮流结果的基础上根据无功精确矩确定无功补偿点及补偿容量,通过重复多次计算得到最优方案。通过vb6.0软件建立电网拓扑模型,结合理论分析,并进行仿真。仿真结果证明,补偿效果较好,软件已在韶关市配电系统中得到试用,并具有良好的实用性,无功优化效果良好。     

A direct second-variational method for unconstrained optimal control problems

A direct second-variational algorithm is proposed for the iterative solution of optimal control problems in continuous, as well as discrete, systems. While the classical method using the Riccati transformation is shown to follow from the direct method, the latter has certain promising features in terms of structural simplicity, ease of programming, and substantial reduction in computation time. The process involves a predictor-corrector algorithm for a forward sweep in the state equations, followed by a backwards sweep in the adjoint equations. Applied to discrete systems, this method functions under conditions less stringent than in the classical scheme. Linearized quasioptimal feedback control is examined from the point of view of the direct method and a scheme for successive improvement in quasi-optimal policy is proposed for systems that suffer small perturbations from a given optimal trajectory.     

A hybrid Improved Quantum-behaved Particle Swarm Optimization–Simplex method (IQPSOS) to solve power system load flow problems

This study proposes a new approach, based on a hybrid algorithm combining of Improved Quantum-behaved Particle Swarm Optimization (IQPSO) and simplex algorithms. The Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is the main optimizer of algorithm, which can give a good direction to the optimal global region and Nelder Mead Simplex method (NM) which is used as a local search to fine tune the obtained solution from QPSO. The proposed improved hybrid QPSO algorithm is tested on several benchmark functions and performed better than particle swarm optimization (PSO), QPSO and weighted QPSO (WQPSO). To assess the effectiveness and feasibility of the proposed method on real problems, it is used for solving the power system load flow problems and demonstrated by different standard and ill-conditioned test systems including IEEE 14, 30 and 57 buses test systems, and compared with the conventional Newton–Raphson (NR) method, PSO and some versions of QPSO algorithms. Furthermore, the proposed hybrid algorithm is proposed for solving load flow problems with considering the reactive limits at generation buses. Simulation results prove the robustness and better convergence of IQPSOS under normal and critical conditions, when conventional load flow methods fail.     

采用改进牛顿法计算配电网理论线损

介绍了传统配电网理论线损计算方法,指出电量法和均方根法存在因未充分考虑负荷曲线变化而容易导致理论线损偏小、管理线损偏大的问题;提出采用改进牛顿法计算配电网理论线损的方案,根据某配电网各变电站24h的电量记录进行电量潮流计算,得出该配电网总线损;分别采用电量法、均方根法、改进牛顿法计算某10kV配电网线路的理论线损,结果表明,与传统配电网理论线损计算方法相比,采用改进牛顿法得出的结果更接近统计线损值。     

A loop-flow-based method for capacitor optimization in distribution systems

A loop-flow-based method for capacitor optimization in distribution systems is proposed in this paper. An optimization algorithm that combines both economic and secure objectives together in a single function for optimal capacitor sizing is presented. Line-current flow, which is a direct indicator of power loss and line loading, is taken as the controlled variable in this optimization algorithm. Therefore, the algorithm has a simple formulation, and is particularly suitable for use in the analysis of radial and weakly meshed distribution systems. An efficient strategy is designed to search for the optimal solution. Because the objective function for the optimization is clearly a quadratic of line-current flow, the convergence character of this algorithm is close to linear and a small iteration is needed in the optimization process. Digital tests have been done to show the validity and efficiency of this algorithm.     

The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems

With the increasing integration of distributed generations (DGs), there is a demand for DGs to play a more important role on the voltage regulation. Meanwhile, the high penetration of DGs could raise a technical problem that the distribution system may operate with bi-directional power flow, leading to the inadequacy of the traditional power flow. Considering this new scenario in distribution system power flow, the convergence theorem is proposed, which contributes to develop a novel selection method of the initial guess closed to the convergent solution. Moreover, to ensure the fast rate of power flow convergence, the theorem of the maximum iterations estimation is also proposed. Based on the two proposed theorems, an Initial Guess Estimation Newton method is proposed, considering different operational status of DGs and initial guess sensitivity simultaneously. Based on the standard node systems, Tongliao grid, and 69 system of USA, three simulation examples are provided to illustrate the effectiveness of the proposed method.      

An efficient algorithm for parameterizing HsMM with Gaussian and Gamma distributions

A widely used method for parameterizing hidden semi-Markov model is using Gaussian distribution to form the output probability and using Gamma distribution to form the state duration probability. Most of these models are based on the classical Newton?s method with second-order convergence, whose iterative convergence speed is slow for large-scale realtime applications. An improved parameter re-estimation algorithm is introduced for such parametric hidden semi-Markov model in this paper. The proposed approach is based on forward and backward algorithm. It applies an iterative method with eighth-order convergence to improve the performance of the model. The numerical examples validate the proposed method.     

基于负熵最大化改进的语音音乐信号分离

负熵是一种重要的非高斯性度量方法,最大化负熵使随机变量的非高斯性达到最大,从而使输出的各分量之间相互独立。负熵最大化算法以负熵作为目标函数,牛顿迭代法作为优化算法,针对牛顿迭代法中对初始值选择敏感的问题,用牛顿下山法代替牛顿迭代法,通过改变下山因子,使目标函数呈下降趋势,降低算法对初始值的依赖性。实验结果表明,改进后的算法在不同初始值下均能较好地分离语音音乐混合信号,改善了初值敏感问题。