异构结构化P2P网络负载均衡方案

结构化P2P网络由于采用DHT算法导致节点存储资源的不均衡,当前解决方案都是假定节点容量及负载是均匀分布在系统中,而忽略了实际网络存在的节点异构性的影响.本文提出的考虑节点异构性的结构化P2P网络负载均衡方案提出了负载均衡的衡量标准--负载平滑度,采用基于相同资源描述符的资源整体转移方案,以节点的邻居节点为平衡范围,描述了系统在节点加入、离开,资源加入以及节点过载情况下的算法,使得整个系统逐步达到负载均衡.该方案充分考虑了实际网络中存在的异构问题.仿真实验表明,该方案有效地解决了并构P2P网络下的负载均衡问题.     

异构无线网络中基于效用的呼叫接纳控制及流量均衡策略

目前国内外对于异构无线网络中联合呼叫接纳控制和流量均衡策略的研究大多停留在网络层以上,即认为不同的网络具有不同的覆盖面积和网络容量,而没有考虑不同网络在物理层和链路层的具体特性.本文在对UMTS和WLAN底层特性分析的前提下,结合应用层为用户设定的效用函数,利用跨层信息提出了一种新的联合呼叫接纳控制及流量均衡策略.仿真结果表明该策略可以使异构无线网络中所有呼叫的效用总和最大.     

基于小波神经网络的电网负荷预测

为提高负荷预测的精度及反映天气因素对负荷的影响.选用小波理论中的Malht算法进行多尺度分析.应用新疆塔北电网近年的负荷数据和气象资料进行了预测,预测结果表明其具有较好的性能.     

Adaptive H∞ event-triggered load frequency control in islanded microgirds with limited spinning reserve constraints

Using an islanded microgrid (MG) with large-scale integration of renewable energy is the most popular way of solving the reliable power supply problem for remote areas and critical electrical users. However, compared with traditional power systems, the limited spinning reserves and network communication bandwidth may cause weak frequency stability in the presence of stochastic renewable active outputs and load demand fuctuations. In this paper, an adaptive event-triggered control (ETC) strategy for a load frequency control (LFC) system in an islanded MG is proposed. First, a bounded adaptive event-triggered communication scheme is designed. This not only saves on network resources, but also ensures that the control center has a sensitive monitoring ability for the MG operating status when the frequency deviations have been efectively damped. Secondly, by fully considering the spinning reserve constraints and uncertain communication delays, the LFC system is described as a nonlinear model with saturation terms. Design criteria for ETC parameters are strictly deduced based on Lyapunov stability theory. Finally, an ETC parameter optimization algorithm based on random direction search is developed to reconcile the bandwidth occupancy and control performance. The efectiveness of the proposed method is verifed in an MG test system.     

Machine-learning based decomposition and monitoring of behind-the-meter resources

With the rapid integration of behind-the-meter (BTM) resources, monitoring the working status of these resources has become a very important task, followed by distributed terminal load decomposition and monitoring can provide real-time power consumption information for the grid and users, which is an effective means to support energy efficiency management and load forecasting. The traditional monitoring method adopts an intrusive design and is difficult to promote and apply on a large scale, so it is of great significance to study proper methods for non-intrusive BTM resources monitoring (NBRM). Load identification is the key to NBRM, based on the characteristic analysis of typical residential loads. The current mainstream load decomposition methods are analyzed, the advantages and disadvantages of the current mainstream methods based on deep learning are sorted out and finally, the results of each algorithm are compared with common data sets.     

Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks (HetNets) and cell densification represent promising solutions for the surging data traffic demand in wireless networks. In dense HetNets, user traffic is steered toward the Low-Power Node (LPN) when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency. However, because of the transmit power differences in different tiers of HetNets and irregular service demand, a load imbalance typically exists among different serving nodes. To offload more traffic to LPNs and coordinate the Inter-Cell Interference (ICI), Third-Generation Partnership Project (3GPP) has facilitated the development of the Cell Range Expansion (CRE), enhanced Inter-Cell Interference Coordination (eICIC) and Further enhanced ICIC (FeICIC). In this paper, we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe (LPS) approach. Our solution allows the separation of User Association (UA) functions at the User Equipment (UE) and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength (max-RSS) based UA scheme, where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system. The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions. Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.     

ANN 电力系统短期负荷预测

采用了优化计算的神经网络结构、可变的学习率η和动量α,选取了能充分反映负荷变化规律的特征量．实例计算表明,方法有效,预测精度比常规方法高,收敛性好,运算速度快     

不规则波振动三轴试验技术的研究

本文通过编制可施加地震不规则剪应变时程的振动三轴试验控制程序,研究了相关的动三轴试验技术问题。     

Web服务器集群的负载均衡中遗传算子的设计

应用遗传算法进行作业调度已被越来越多的学者关注。在Web服务器集群环境中,对于客户端的Web请求分配问题,采用常规的遗传算法进行负载均衡并不总是有效的,好的遗传算子对算法收敛性及收敛到最优解非常重要。基于集群环境中Web请求分配的特点,设计了有针对性的遗传算子,即改进的内外结合交叉算子和主动变异算子。模拟实验结果与分析表明这些算子对Web集群的请求分配是有效的。     

基于内容的网络集群负载平衡算法模型

在论述网络集群负载平衡算法的基础上,基于内容分类的方法,给出基于内容的网络集群负载平衡算法三元组模型。请求分类有利于提高缓存命中率,调度机制说明如何适当地转发请求,动态反馈避免将请求分配到重载的服务器,进而分析了调度机制的八种调度策略和六种基于内容的调度转发技术。该模型利用缓存内容来提高集群的吞吐量和响应时间,可部署多种服务类型。