基于CPSO-BP神经网络的风电并网暂态电压稳定评估

针对目前传统方法难以快速、准确判断风电并网后系统暂态电压稳定性的问题,提出了一种基于CPSO-BP组合的风电并网暂态电压稳定评估方法。首先采用混沌理论对粒子群算法的不足进行改善,应用改进后的算法对神经网络的初始权值和阈值进行优化,然后利用系统故障前后采集的传统物理量和风电场相关的物理量作为BP神经网络输入特征量进行监督学习,最后将训练得到的模型应用于风电并网系统的暂态电压稳定评估中。利用英格兰10机39节点系统标准算例进行风电并网仿真分析,结果证明了所提方法的有效性。     

国产羟丙基甲基纤维素在高聚合度聚氯乙烯生产中的应用

介绍了国产羟丙基甲基纤维素替代进口羟丙基甲基纤维素在高聚合度聚氯乙烯聚合中的应用,考察了两种羟丙基甲基纤维素对高聚合度聚氯乙烯树脂各项指标的影响.结果 表明,国产羟丙基甲基纤维素替代进口羟丙基甲基纤维素是可行的.     

环磺酮代谢物合成工艺研究

针对环磺酮代谢物开展制备工艺研究.以环磺酮为原料,经拜耳-维利格氧化重排等反应制备得到3个代谢物,含量均大于98.5％,满足用于农药登记、农残质检等领域的应用,极具社会应用价值.     

一步法制备2-氟苯甲醚

以邻二氟苯、甲醇钠为原料,甲苯、四氢呋喃为溶剂,一步醚化法制备了2-氟苯甲醚,用1H NMR表征了该化合物结构.考察了不同原料配比、反应温度和反应溶剂对目标产物收率的影响,在优化条件下,目标产物的收率为84.8％.该路线操作简单,收率高,对环境友好,适合工业化生产.     

计及需求响应的含间歇式可再生能源电力系统的多目标优化调度

建立计及需求响应的含风光发电电力系统的多目标优化调度模型.将实时电价和可中断负荷两种需求响应项目融入模型中,引入备用容量成本系数和碳排放价格,并根据实时电价对弃风量进行经济惩罚,以经济和环保成本之和最小建立目标函数.对可再生能源的收购政策、备用容量提供方案以及两种需求响应项目的效果进行比较,并分析模型参数的敏感性.算例分析表明:全额收购光电、允许适当弃风的方案使调度更加灵活;需求响应降低了风电波动对系统的影响,优化了机组组合方案,提高了系统的经济和环境效益.     

考虑多元可控负荷协调的配网鲁棒优化经济调度模型

为有效应对大规模分布式可再生能源和可控负荷接入配网后在经济优化调度方面所带来的挑战,本文建立考虑多元可控负荷协调的配网鲁棒优化经济调度模型.首先考虑电池储能系统、电动汽车及可参与需求侧管理的电负荷的能量转移特性,建立多元可控负荷可调模型;进而考虑配网中多元可控负荷的调峰能力及动态约束以运行成本最低建立配网鲁棒经济优化调度模型;最后,以蒙东某配网实际数据为例,在改进IEEE 14节点配电系统上进行调度仿真分析,验证本文所提出模型可充分利用多元可控负荷的调节能力,有效提升配网运行经济性.     

Thermal cross-linking and anti-meltdrop properties of copolyester containing phosphorus/magnesium salt composites by in situ polymerization

Polyester is widely used in household products because of its good mechanical properties and wears resistance, but polyester is easy to ignite and inclined to produce droplet, so its application range is limited. The cross-linkable magnesium hydroxide nanoparticles were incorporated into flame-retardant polyester, which enables the phosphorus-containing copolyester with thermal cross-linking and anti-meltdrop properties. The nanoparticles were achieved by in situ polymerization and acted as a nucleating agent for improving the crystalline properties of the copolyester. Furthermore, the nanoparticles also enhanced anti-meltdrop properties and reduced the heat and gas release during the combustion process of the copolyester. The maximum heat release rate and total smoke release reduced by 39.8% and 74.4% compared with pure polyester. Specifically, the combustion products of the nanoparticles and phosphorus flame retardant could act a barrier role by covering the carbon layer to isolate air and heat, thereby resulting in excellent anti-meltdrop properties. The simple modification method reported here realizes the collaborative modification of flame retardant and anti-meltdrop properties of phosphorous flame-retardant copolyesters by thermal cross-linking.     

蜂窝网络中D2D通信资源分配方法综述

随着第五代(the fifth generation,5G)移动通信系统商用进程的推进,设备到设备(device-to-device,D2D)通信越发受到人们的关注.频率、功率等资源分配作为优化D2D通信的关键技术成为重要的研究课题,因此对蜂窝网络中D2 D通信资源分配研究进行综述.首先介绍D2 D通信模型和模式;其次从数学理论角度出发,分析蜂窝网络中D2 D通信资源分配的主要方法:基于图论、超图理论、博弈论、机器学习和启发式算法等,对这些算法进行对比分析;最后总结上述D2 D资源分配方法存在的主要不足,并对蜂窝网络中D2 D通信的未来发展进行展望.     

Edge diffraction correction for 3D scattering center modeling based on the shooting and bouncing ray technique

In the traditional scattering center extraction method based on the shooting and bouncing ray(SBR) technique,only the physical optics contribution of the target is considered.However,the physical optics method is unable to describe the contribution of the actual edge diffraction.By comprehensively considering the specular reflection and edge diffraction contribution of the target,an edge diffraction correction method for 3D scattering center modeling based on the SBR technique is proposed.Using the SBR technique and combining the image domain ray tube integration and equivalent edge currents method,the edge diffraction correcting formula for the 3D inverse synthetic aperture radar image is derived.Simulation results show that the proposed method is able to effectively improve the modeling accuracy of the 3D scattering center.     

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.