大段长高压电缆护层保护器暂态特性分析与参数优化设计

大段长高压电缆在运行时会产生过高的护层感应电压,这对电缆护层保护器的过电压防护特性提出了更高要求,因此需要针对大段长电缆护层保护器暂态特性进行分析研究。基于PSCAD仿真软件,建立了典型220 kV高压电缆线路仿真模型,得出了在短路过电压和雷击过电压情况下,高压电缆长度对护层感应电压暂态特性的影响。通过绝缘配合与能量保护相结合的方式,得到了保护器参数取值范围及电缆线路短路电流与长度的适配曲线,提出了护层保护器参数优化设计的具体方法,并进行了试验测试。测试结果表明:当电缆线路出现短路故障时,随着电缆长度的增加,护层感应电压先线性增长,随后在保护器残压阈值的限制作用下逐渐趋于"饱和"状态,现有保护器能量吸收能力难以满足大段长电缆需求;改进后的护层保护器能量吸收能力显著提升,20 k A短路电流时允许的电缆长度大幅提高,满足大段长高压电缆线路安全运行的要求。     

Operational optimization of a building-level integrated energy system considering additional potential benefits of energy storage

As a key component of an integrated energy system (IES), energy storage can effectively alleviate the problem of the times between energy production and consumption. Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems. This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system (BIES) considering additional potential benefits of energy storage. Based on the characteristics of peak-shaving and valley-filling of energy storage, and further consideration of the changes in the system’s load and real-time electricity price, a model of additional potential benefits of energy storage is developed. Aiming at the lowest total operating cost, a bi-level optimal operational model for day-ahead operation of BIES is developed. A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation. The results demonstrate that the model can exploit energy storage’s potential, further optimize the power output of BIES and reduce the economic cost.     

基于模糊逻辑的电动汽车双源混合储能系统能量管理策略

针对电动汽车行驶过程中电池放电电流过大导致的电池容量衰减问题,构建了由锂离子动力电池、超级电容和多端口DC/DC变换器构成的全主动式混合储能系统,其中电流环控制器和电压环控制器分别控制输出电流和直流母线电压。结合超级电容SOC、整车需求功率和车速情况,根据建立的45条模糊控制规则,模糊逻辑控制器调节锂离子动力电池和超级电容的充放电功率,在车辆峰值功率需求较高时避免了高频电流波动对动力电池寿命的影响。同时在功率需求较低时,动力电池给超级电容充电。在HWFET工况下的实验结果表明所提出的全主动式双能量源混合储能系统和基于模糊逻辑的能量管理策略能够有效保护锂离子动力电池免受大电流波动影响,从而达到延长电池寿命的作用。     

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling

Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical ‘Geometry, Frequency, Noncovalent, eXtended Tight Binding’ (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent ‘Statistical Assessment of the Modeling of Proteins and Ligands’ (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.     

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.     

Optimization algorithm for ultra-sparse arrays in regional energy focusing

The setting of ultra-sparse arrays is one of key factors for the performance of regional energy focusing.This paper focuses on one of its typical applications:Precision Electronic Warfare by UAV,and proposes an optimization algorithm for ultra-sparse arrays to maximize the performance of regional energy focusing.First,we establish the joint optimization model of the interference waveform and the positions of elements.Second,particle swarm optimization (PSO) is adopted to iterate the positions of elements.Then,the target value of the subproblem is solved for the fitness criterion,as the current array is known in each step of the iteration.Finally,we get the optimal solution of the interference waveform and the ultra-sparse array by this method.Numerical results indicate that the algorithm can optimize the array effectively,and improve the indicators of regional energy focusing obviously.Whether the localization errors of elements exist or not,the algorithm provides a better performance than existing algorithms which only design the interference waveform.     

NDN数据提供者移动支持策略能耗建模与分析

基于数据提供者的移动节点在网络中的不同位置及不同移动速度,结合命名数据网络（NDN）请求者驱动的数据传输模式,提出了3类NDN移动支持策略的网络能耗解析表达式,为NDN移动网络的研究提供了形式化的建模方法.对这3类移动支持策略在不同场景下能耗的分析与比较结果表明,数据流行度分布、移动节点所处位置及移动速度对策略的能耗影响不尽相同,为后续探求最优能效的移动支持策略提供了理论参考.     

车联网基于信任度的RSU识别及资源分配算法

在车联网中,由于部分路侧单元（RSU）的位置比较偏远,无法得到网络管理员的及时维护,可能遭到攻击,变为不可信任的路侧单元,降低了车辆的通信质量和网络能效.为了有效地识别RSU的可靠性,提出了一种基于信任度的路侧单元识别及资源分配算法,将路侧单元识别、传输模式的选择和功率分配联合建模为网络能效的优化问题,并分成3个子优化问题,分别求解.首先,采用基于信任度的RSU识别算法,识别出可信任和不可信任的路侧单元;其次,优化车辆资源块配置和链路选择;最后,通过次梯度算法优化传输功率,最大化网络能效.理论分析和仿真结果表明,所提算法具备较低的复杂度,可以达到较高的路侧单元识别准确度,并有效地提升网络总能效.     

基于纳米压痕技术的电子玻璃微观力学性能研究

为了研究不同电子玻璃的微观力学性能,采用先进的纳米压痕技术记录钠钙硅、无碱硼铝硅和碱铝硅等典型电子玻璃的载荷-位移曲线,利用Oliver-Pharr方法和经典的弹塑性变形理论,计算玻璃的硬度和弹性模量. 玻璃的硬度主要与结构的键合度相关,平均非桥氧数越高,外力作用下越容易致密化,硬度越小;弹性模量主要取决于质点间的化学键强度,化学键力越强,变形越小,弹性模量越大;九点法测得的弹性模量与硬度的变化趋势不完全相同,借助硬度-弹性模量-能量耗散之间的本征关系,评价玻璃样品的微观均匀性,其中无碱硼铝硅玻璃的恢复阻力大,局部能量耗散大,不容易引起整体破坏,力学性能最好;与浮法工艺相比,溢流下拉法制备样品的局部力学性能波动较小,微观均匀性较好.     

排桩式波浪能发电装置附近流场特性研究

基于雷诺时均Navier-Stokes方程和k?ω湍流模型,研究单排桩式振荡水柱式波浪能发电装置在规则波作用下的桩基附近流场特性. 通过物理模型实验验证所建数值波浪水槽的准确性. 模拟规则波作用下装置附近的流场特性,分析装置附近的涡特征以及其对装置附近泥沙冲刷情况的潜在影响. 结果表明,测试工况下,在桩式振荡水柱装置桩柱体的桩基附近观察到马蹄涡和尾涡现象,马蹄涡强度随着Keulegan–Carpenter（KC）数的增大而减小,尾涡强度随着KC数的增大而增大. 马蹄涡因强度过小,在模拟的KC数范围内不是装置桩基泥沙冲刷的主要因素,高强度的尾涡很可能成为装置桩基附近泥沙起动、输运和冲刷的重要影响因素.