NOVEL COMPOSITE CVT SYSTEMS WITH DIVERGING AND DUAL-MODE CONVERGING POWER FLOW

A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated manual transmissions) become continuously variable, isstudied. With specific mechano-mechanical and electromechanical composite CVT systems as detailedexamples, its basic working principles are expatiated. General methods and key points in designingand realizing such systems are also analyzed and discussed.

Reactive Power Flow Convergence Adjustment Based on Deep Reinforcement Learning

Power flow calculation is the basis of power grid planning and many system analysis tasks require convergent power flow conditions.To address the unsolvable power flow problem caused by the reactive power imbalance,a method for adjusting reactive power flow convergence based on deep reinforcement learning is proposed.The deep reinforcement learning method takes switching parallel reactive compensation as the action space and sets the reward value based on the power flow convergence and reactive power adjustment.For the non-convergence power flow,the 500 kV nodes with reactive power compensation devices on the low-voltage side are converted into PV nodes by node type switching.And the quantified reactive power non-convergence index is acquired.Then,the action space and reward value of deep reinforcement learning are reasonably designed and the adjustment strategy is obtained by taking the reactive power non-convergence index as the algorithm state space.Finally,the effectiveness of the power flow convergence adjustment algorithm is verified by an actual power grid system in a province.

Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Higher-order expansions of powered extremes of logarithmic general error distribution

In this paper,Let M_(n)denote the maximum of logarithmic general error distribution with parameter v≥1.Higher-order expansions for distributions of powered extremes M_(n)^(p)are derived under an optimal choice of normalizing constants.It is shown that M_(n)^(p),when v=1,converges to the Frechet extreme value distribution at the rate of 1/n,and if v>1 then M_(n)^(p)converges to the Gumbel extreme value distribution at the rate of(loglogn)^(2)=(log n)^(1-1/v).

Convergence Properties of Piecewise Power Approximations

We address the problem of convergence of approximations obtained from two versions of the piecewise power-law representations arisen in Systems Biology. The most important cases of mean-square and uniform convergence are studied in detail. Advantages and drawbacks of the representations as well as properties of both kinds of convergence are discussed. Numerical approximation algorithms related to piecewise power-law representations are described in Appendix.

Enhancing Iterative Learning Control With Fractional Power Update Law

The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.

A Dual-Mode Step-up DC/DC Converter IC with Current-Limiting and EMI Reduction Techniques

This paper presents a novel dual-mode step-up （boost） DC/DC converter. Pulse-frequency modulation （PFM） is used to improve the efficiency at light load. This converter can operate between pulse-width modulation （PWM） and pulse-frequency modulation. The converter will operate in PFM mode at light load and in PWM mode at heavy load. The maximum conversion efficiency of this converter is 96%. The conversion efficiency is greatly improved when load current is below 100 mA. Additionally, a soft-start circuit and a variable-sawtooth frequency circuit are proposed in this paper. The former is used to avoid the large switching current at the start up of the converter and the latter is utilized to reduce the EMI of the converter.

Evaluation of Exponential Integral by Means of Fast-Converging Power Series

Exponential integral for real arguments is evaluated by employing a fast-converging power series originally developed for the resolution of Grandi’s paradox. Laguerre’s historic solution is first recapitulated and then the new solution method is described in detail. Numerical results obtained from the present series solution are compared with the tabulated values correct to nine decimal places. Finally, comments are made for the further use of the present approach for integrals involving definite functions in denominator.

改进差分进化的光伏最大功率点跟踪

针对现有差分进化算法在局部遮阴情况下跟踪速度慢的问题,提出一种新的改进差分进化算法的光伏发电系统最大功率点跟踪控制策略。首先,引入三角突变策略,并将其嵌入传统差分进化算法的交叉公式当中,避免因迭代前后输出相同的占空比造成重复采样功率的情况。其次,引入自适应比例因子策略,以加强个体的收敛性。最后,通过奖励惩罚机制平衡所提算法的全局开发和局部探索能力。仿真结果表明,与对比文献的差分进化算法相比,改进算法在跟踪速度、稳定性方面具有显著的优势。

运营商视角下的算力网络技术及实践研究

算力网络是云化网络发展演进的下一个阶段,是运营商摆脱“管道”提供商困境、支撑业务高质量发展的必然选择。从算力网络的本质内涵出发,在对算力网络架构及实现路径深入分析的基础上,通过某运营商的算力网络实践阐述如何打造一体共生的精品算力网络,支撑数字经济高质量发展、赋能千行百业。

光伏汇集组网系统对电网电压支撑特性研究

大规模新能源开发是践行“双碳”目标的必然路径。该文主要针对不同光伏汇集组网系统对电网的电压支撑特性进行研究。在大规模新能源开发背景下对汇集组网系统技术要求的归纳基础上,对比分析了大规模新能源的汇集组网方式,并提出一种集中串联升压组网输出的直流汇集拓扑;对某大规模光伏地区电网和多种汇集组网方式进行了等值建模;对比仿真分析交流、直流汇集组网方案对电网的电压支撑特性的结果,验证了所提串联升压组网输出直流汇集方式在电压支撑性方面优于传统交流汇集组网方式。

基于数字孪生的配电网潮流网络模型构建研究

由于配电网中导线重复接地对潮流计算收敛性的影响,无法获取精准配电网潮流求解结果,为此,提出了基于数字孪生的配电网潮流网络模型构建研究。根据配电网数字孪生技术架构,计算配电网鲁棒最优潮流。依据配电网对电机出力和负荷需求,构建基于鲁棒优化的鲁棒约束函数,引入保守度调节参数使调控安全性与经济性达到平衡状态。分析配电网潮流网络拓扑结构,结合预设的特征数据类型与特征关联信息之间关系,确定配电网潮流方向。结合数字孪生技术迭代处理注入电压,完成数字孪生潮流网络模型构建。实验结果表明,该模型收敛次数较多,最短收敛时长为1.30 s,具有良好的收敛性。

Rates of convergence of powered order statistics from general error distribution

Let{Xn:n≥1}be a sequence of independent random variables with common general error distribution GED(v)with shape parameter v>0,and let Mn,r denote the r-th largest order statistics of X1,X2,...,Xn.With different normalizing constants the distributional expansions and the uniform convergence rates of normalized powered order statistics|Mn,r|p are established.An alternative method is presented to estimate the probability of the r-th extremes.Numerical analyses are provided to support the main results.

全纯系数形式幂级数的收敛集

该文研究形式幂级数f(z,t)=∑_(n=0)^(∞)fn(z)t^(n)的收敛集,这里系数fn(z)是复平面上某个域Ω上的全纯函数.Ω的一个子集E被称为Ω上的收敛集,如果存在形式幂级数f(z,t)使得E恰好包含使得f(z,t)作为t的幂级数在原点的某个邻域内收敛的所有z.σ-凸集被定义为可数个多项式紧凸子集的并.证明了复平面的子集是收敛集当且仅当它是σ-凸的.

算网城市发展路径研究

随着数字技术与新型城镇化深度融合,算网城市将成为未来城市基础设施建设的重要组成。算网城市以算力基础设施为底座,以算力资源统筹调配和运营管理为抓手,通过构筑城市算力“一张网”,赋能城市数字化转型和发展,对实现我国数字中国建设目标具有重要作用。梳理了算网城市建设背景,分析了算网城市的内涵架构,明确了算网城市建设意义,并进一步提出算网城市的建设路径。

基于改进自适应协同控制方法的电力系统混沌控制

针对4阶混沌电力系统,该文提出一种具有快速收敛特性的自适应协同控制方案。首先基于Lyapunov稳定性定理和全局快速收敛理论,设计了一种具有快速收敛特性的协同控制器,该控制器可使宏变量快速到达不变流形并可以得到平滑无抖振的控制输入,实现宏变量的精确收敛。然后将所设计的控制器应用于4阶电力系统的混沌控制中。由于电力系统中的能量过剩会引起混沌振荡,在控制回路中引入储能装置,通过控制储能装置吸收电力系统中多余的有功功率来抑制其混沌振荡。在此基础上通过设计自适应律,消除了控制器设计过程中出现的复杂项,增加了控制器的实用性。最后通过数值仿真,验证了该控制方案的有效性与优越性。

基于新型幂次指数趋近律滑模控制算法的研究

针对传统滑模控制存在抖振与收敛速度缓慢的问题,提出一种新型幂次指数趋近律的滑模控制方法。首先上述趋近律结合了双曲正切函数与符号函数的特征进行了切换函数上的改进,滑模系数影响因子引入了系统误差与滑模状态变化量,使其能够自适应调节滑模到达过程的收敛速度并对其进行分阶段调节,还利用切换函数的特征分析了趋近滑模面双侧切换区间的范围;其次分析了自适应新型幂次指数趋近律的收敛区域和收敛时间,并证明了此趋近律的存在性、可达性与稳定性;最后将新型幂次指数趋近律和其它各种传统趋近律运用到经典二阶系统进行了对比,仿真结果表明新型幂次指数趋近律能够更好的提升收敛速率以及削弱抖振的产生。

幂级数和函数的求法探究

通过幂级数的运算求和函数是《数学分析》学习中的难点之一。通过分类列举的方式,根据幂级数的两个性质:在收敛域内的任何闭区间上是一致收敛的;逐项求导、逐项积分后收敛半径不变,但收敛域有可能改变,对幂级数和函数的求法在四个角度进行归纳总结,形成比较全面的解题策略,有利于帮助学习者熟练掌握幂级数的运算。

行政审批中权力寻租的惩治困境与解决路径——行政法与监察法的交叉视角

行政审批中权力寻租问题既关乎社会资源的分配,又影响着全国统一大市场的建设;既关乎行政法领域,又涉及监察法领域。因此在行政法与纪检监察法的交叉视角下对行政审批中权力寻租的基本特征、种类、表现方式和危害等方面展开“解剖式”分析,探讨三大惩治困境:行政法与监察法衔接难,表现为两法中的滥用职权范畴不一致;监督难,表现为法院的法律监督缺乏处置权限和纪委监委的监督缺乏专业性;预防难,表现为减少利益驱动难和纠正违法犯罪难。针对三大惩治困境,具体的解决路径有:加强立法衔接,将“限制公民权利、增加公民义务”作为权力寻租违法与犯罪间危害后果的标准,将行政诉讼法中的“滥用职权”与“明显不当”统称为“滥用自由裁量权”;从严惩处,并罚受贿与滥用职权,追究党员的双重责任;减少利益驱动因素,对公正履职的行政审批人员予以政治性、经济性和荣誉性激励,对权力寻租的行政审批人员予以通报曝光。