含逆变型分布式电源的不平衡配电网短路电流计算方法研究

逆变型分布式电源（IBDG）的输出由其控制策略决定而具有很强的非线性,使传统配电网的短路电流计算方法不再适用,而配电网的不对称更是进一步增加了其短路计算的难度。根据对称分量法推导出不平衡配电网的故障点各序电流,提出一种基于序网络迭代修正的不平衡配电网短路电流计算方法,在每一次迭代中,根据当前节点各序电压分别修正IBDG的输出电流和各不对称元件补偿电流源的输出电流,利用节点电压方程得到新的节点各序电压和故障点各序电流,直到满足收敛条件。最后,基于PSCAD/EMTDC对含IBDG不平衡配电网的进行了仿真计算,验证了所提方法的准确性和有效性。     

基于电流变化的环状直流配电网故障定位方法

针对现有故障测距方法无法同时对单极接地故障和极间故障进行定位,以及利用电容放电阶段进行故障定位时忽略了多端换流站都会对故障点注入电流等问题,提出一种利用故障线路上的全电流分量对多端柔性直流配电网进行故障定位的方法。通过联立与故障点相邻两网孔的电压方程,从算法上解决了过渡电阻对求解故障位置的影响。在simulink中搭建6端环状模型对所提方法进行仿真,结果验证所提方法可以在发生线路故障时能快速识别故障并进行可靠定位,满足直流配电网的故障测距要求。     

利用零序电流倍增特征的不接地系统故障选线

针对中性点不接地系统单相接地故障选线困难的问题,通过母线故障相接地故障转移与中性点接入小电阻的方式来增强故障信息从而选出故障线路。建立了故障转移阶段、并联电阻投入前后各个阶段的零序网络模型,分析了健全线路和故障线路各自的零序电流幅值的变化特征。在中性点小电阻投入、母线开关分闸前后各馈线零序电流幅值变化明显,健全线路零序电流在母线开关分闸后显著减小而故障线路零序电流增大。利用故障线路与健全线路的零序电流变化特征构建了不接地系统故障选线判据。大量的仿真计算验证了该方法对于不接地系统单相接地故障良好的适应性,有助于进一步提高故障选线准确率。     

基于高频暂态稀疏测量的复杂直流配电网故障定位

为了解决直流配电网中高速通信测量点不足、故障特征复杂等导致的故障定位精度差的问题,提出了一种适用于复杂直流配电网的新型直流极间短路故障定位算法。首先,根据高频瞬态电流环路,构建含有电平转换器和DC/DC转换器的高频阻抗等效模型为故障过程提供稳定的阻抗值。其次,利用BCS理论推导与稀疏测量点相对应的节点高频暂态电压方程。最后,结合节点高频瞬态电压方程和贝叶斯压缩感知理论,求解节点高频瞬态电流稀疏矢量实现故障定位。实验结果表明:所提算法对测量点的数量要求较低,不需要严格同步地测量数据,且不受转换器的控制策略和过渡电阻影响,具有较高的故障定位准确性。     

Video multipath transmission mechanism with load balancing in the FiWi network

Aiming at the differentiated needs of users and the problem of video service interruption caused by the local overload of the converged network,a load-balanced video transmission mechanism is proposed.First,the split multipath routing protocol is improved to obtain the path selection model according to the front-end structural characteristics of the optical fiber wireless access network;second,we calculate the path differential delay and the video transmission delay on wireless side,and set their threshold as the quality of the experience constraint.Finally,while considering the quality of user experience,a particle swarm optimization algorithm with a multi-stage penalty function is used for video distribution.Simulation results show that the proposed mechanism is better than the throughput-aware load-sensing algorithms,enhances delay-control load distribution algorithms and delay-energy-quality aware multipath algorithms,and that it can effectively balance the network load and alleviate network congestion while avoiding user loss.     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.     

T-NTRU IoT dynamic access authentication technology

The secure access authentication of the Internet of the things terminal is the key technology to ensure the large-scale construction of the power Internet of things.The elliptic curve cryptography (ECC) algorithm is usually used in the transmission authentication scheme,and it requires a large amount of calculation.Furthermore it is proved that its security against quantum attacks is poor.The number theory research unit (NTRU) algorithm can resist quantum attacks,and its computational speed is faster than that of the ECC.This paper proposes a dynamic T-NTRU secure access authentication algorithm based on time information in the Internet of things.It uses the dynamic changing time series as the secret key of the hash function to solve the internal attack security problem caused by the fixed hash function.Experiments are carried out on the computer and single chip microcomputer,respectively.Experimental results show that compared with the traditional ECC calculation,the T-NTRU algorithm proposed in this paper reduces about 97% of the calculational amount,which is equivalent to the typical NTRU algorithm,and is suitable for the need of the resource constrained power IOT network application.     

基于机器学习的网络拥塞控制研究

拥塞控制是网络研究的经典课题,可以避免网络因拥塞而性能下降。其在互联网的发展中扮演着重要的角色。近年来,随着机器学习、深度学习和强化学习的兴起,给拥塞控制提供了新的思路。对网络拥塞控制的机制进行了详细分析,阐述了国内外对于该领域的研究现状及进展,将有代表性的解决方案分为基于规则的解决方案、基于路由反馈的解决方案和智能解决方案3类,并详细分析了各方案的原理及优缺点。     

基于ArcReLU激活函数的优化研究

对常用的激活函数进行了研究比较。对曾经提出的一种新型激活函数ArcReLU进行了改进,使其具有更快的收敛速度和更低的计算消耗。实验证明,改进的ArcReLU函数既能显著加快反向传播神经网络的训练速度,又能有效降低训练误差,甚至能避免梯度消失的问题。     

基于MODWT和BP神经网络的微电网故障诊断方法

近年来,随着微电网技术的持续发展,电力用户对其供电可靠性的要求也不断提高,因此微电网故障诊断研究也变得越来越重要。提出了一种基于极大重叠离散小波变换(MODWT)和反向传播(BP)神经网络的微电网故障诊断新方法,并通过仿真与算例进行了验证。结果表明:该方法能快速、准确地识别出故障类型,且不受故障初始相位角和过渡电阻等因素的影响;与现有的基于离散小波变换和反向传播神经网络的诊断方法相比所提出的方法可以提供更好的故障分类精度。