Design and validation of real-time dynamic spectrum management in OFDM-based HNPLC systems

Time-varying frequency selective attenuation and colored noises are unfavorable characteristics of power line communication(PLC) channels of the low voltage networks.To overcome these disadvantages,a novel real-time dynamic spectrum management(DSM) algorithm in orthogonal frequency division multiplexing(OFDM)-based high-speed narrow-band power line communication(HNPLC) systems is proposed,and the corresponding FPGA circuit is designed and realized.Performance of the proposed DSM is validated with a large amount of network experiments under practical PLC circumstance.As the noise in each narrow subcarrier is approximately Gaussian,the proposed DSM adopts the BER/SER expression formulized via the AWGN channel to provide a handy and universal strategy for power allocation.The real-time requirement is guaranteed by choosing subcarriers in group and employing the same modulation scheme within each transmission.These measures are suitable for any modulation scheme no matter the system criterion is to maximize data rate or minimize power/BER.Algorithm design and hardware implementation of the proposed DSM are given with some flexible and efficient conversions.The DSM circuit is carried out with Xilinx KC705.Simulation and practical experiments validate that the proposed real-time DSM significantly improves system performance.

Application study of dynamic voltage scaling policies

Based on the fundamental relationship among the circuit power, the circuit delay and the supply voltage, four theorems associated with the application of dynamic voltage scaling （DVS） policies are proposed and proved. First, the existence characteristics of the optimal supply voltage for a single task are proved, which suggests that the optimal supply voltage for the single task should be selected only within a one-dimensional term, and the corresponding task end time by the optimal supply voltage should be identical with its deadline. Then, it is pointed out that the minimum energy consumption that the DVS policy can obtain when completing a single task is certainly lower than that of the dynamic power management （DPM） policy or the combined DVS＋DPM policy under the same conditions. Finally, the theorem of energy consumption minimization for a multi-task group is proposed, which declares that it is necessary to keep the processor in the execution state during the whole task period to obtain the minimum energy consumption, while satisfying the deadline constraints of any task.

基于随机模型的适应性DPM算法在Linux下的实现

针对当前电子设备能耗不断增加的问题,对Linux下的动态电源管理进行了研究,在介绍一种基于随机模型的适应性动态电源管理(DPM)算法的基础上,详细给出了该算法在Linux下的实现方案:观察器的代码驻留在内核中,用来观察系统资源的使用情况;策略优化过程利用PCx进行线性规划求解;控制器通过发出硬件命令直接控制硬件.试验结果表明,所提出的DPM算法可以有效地降低系统功耗,在实现中不需要对Linux内核进行很大的修改,对系统性能的影响几乎可以忽略.

基于DPM和DVS的双效节能调度算法

对于电池供电系统,如何降低功耗已成为系统设计中的一个关键问题。通过对系统功耗组成及任务调度策略进行深入分析,提出一种动态电压调节(DVS)和动态功率管理(DPM)相结合的双效节能调节算法DVS-PM。DPM动态管理处理器及外围模块在工作模式和低功耗模式之间切换,DVS在保证系统实时性前提下,实现系统运行在功耗最低的最佳工作点,达到更好的节能效果。实验结果表明,该节能调度算法能大幅减少系统能量消耗,与不采用节能算法相比,节能55.4%。

PowerPC 603e低功耗技术的开发

Power PC6 0 3e是一款高性能、低成本、低功耗的 32位超标量 RISC微处理器。本文简要介绍了 Power PC6 0 3e结构 ,详细介绍了 Power PC6 0 3e动态功耗管理、静态功耗管理设计技术及一些低功耗技术 ,最后给出了 Power

基于Linux动态频率调整的DPM方案

在嵌入式系统中,提出一个基于Linux动态频率调整(DFS)的动态电源管理(DPM)方案。该方案可以实现2个及2个以上级别的频率调整,为用户提供系统频率等级设定接口,从而增加用户自主选择功能,解决单纯依靠系统自动调整频率等级的数量限制问题,并将应用层、内核层、驱动层程序分开以增强代码可移植性。在S3C2440A平台的实验结果验证了该方案的可行性。

一种提高Ad Hoc网络节点能量效率的DPM模型

在节点能量有限的Ad Hoc网络中,如何延长节点电池寿命并提高能量效率是能量问题研究的关键之处。针对这一问题,提出了移动节点的动态策略管理(DPM)模型,并在模型中引入了随机优化控制策略以提高节点能量效率。然后应用概率模型检测技术将其建模为DTMCs,并使用PRISM对这一策略和DPM中另外两种常用预测策略进行了比较,结果表明这一优化策略对网络的拥塞、延时及可靠性影响较小,其总体节能效果优于上述两种预测策略。

使用遗传算法预测空闲期长度的DPM策略

减少功耗已经成为计算系统设计面临的一个主要挑战。动态功耗管理策略利用部件的空闲期节约能量,在足够长的空闲期可以把部件置于较低的功耗状态以节约能量。论文提出一种新的动态功耗管理策略:考虑到设备负载的突发性,把设备的负载划分为负载基因,把设备使用请求的到达过程转化为负载基因进化过程,然后使用遗传算法预测未来的负载基因,进而预测部件的空闲期长度。实验结果表明,提出的这种策略具有较高的预测精度,因此具有更好的节能效果。

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems.With the advancement of technology,the performance management of central processing unit(CPU)is changing.Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size.When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life cycle.In this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption.The selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)policy.Increase in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more power.The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs.The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs.Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor systems.The performance measurement of computational systems is changing with the advancement in technology.Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies.The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the chip.In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration.Task migration avoids peak temperature values in the multicore system.High utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature on-chip.Our technique switches the core bymigrating such task to a core that has less temperature and is in a low power state.The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core.The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works.The simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

含电动汽车和光伏的配电网演化动态博弈调度策略

针对大规模电动汽车的无序充电行为可能导致电网出现的三相不平衡问题,基于演化动态博弈模型,提出一种考虑三相负荷平衡和光伏消纳的电动汽车充电调度策略。首先,基于电动汽车接入时段的主观性以及光伏出力和负荷的不确定性,引入游牧集群和定居集群之间的演化动态博弈方程;然后,考虑用户侧和电网侧双方利益关系,设计以最小化电动汽车充电费用、三相负荷不平衡度和无功需求为目标的调度优化模型;最后,通过对某商业区配电网算例进行仿真求解,对比分析三相有功曲线、三相无功曲线以及荷电状态曲线的变化趋势。算例结果表明,该调度策略可有效降低三相不平衡度,满足无功补偿的需求以及改善用户充电成本。

大数据环境下基于BIM与CNN的电力工程造价优化算法

针对大数据环境下电力工程造价在精准化、动态化等方面存在的不足,提出了一种基于BIM与CNN的电力工程造价优化算法。利用BIM技术的特点进行电力工程全生命周期的造价管理,实现了造价的动态化管控。并且采用Levenberg-Marquardt规则算法改进卷积神经网络,通过改进后的CNN网络对每个工程环节的造价完成预测,从而优化整个工程的施工方案。结合相关的电力工程造价数据,基于Matlab对所提算法进行实验测试。结果表明,当学习率为0.010时CNN网络的性能最佳,所提算法的预测准确率为94%,并且与造价的真实值最为接近。

低功耗海上救援个人示位标的设计与实现

设计和实现了一种改进的海上救援个人示位标,该示位标具备北斗导航定位能力和北斗短报文通信功能,同时具备人体生命体征监测上报和触水启动的功能,并支持COSPAS-SARSAT卫星搜救系统.为延长示位标的工作时间,设计了一种动态电源管理方法,实现了对不同功能电路电源通道的控制.同时,针对示位标的待机模式和工作模式,设计了超低功耗待机电路和低功耗软件控制设计方案,减少了电能的浪费.最后,分别在山东省威海市和浙江省宁波市至舟山市附近海域对原型样机进行了实际场景测试.原型样机的测试结果表明:示位标在待机状态下,电流为2.3μA,待机时间超过5年;示位标在接收北斗信息状态下,电流为79 mA;示位标在发送状态下,7 000 mAh 7.4 V电池可发送200次求救信息.与现有产品相比,本文提出的海上救援个人示位标具有更低的待机功耗.

适用于海洋动态观测设备嵌入式系统的低功耗管理预测模型

能耗是制约海洋设备在水下工作时长的关键因素。为进一步降低海洋动态观测设备嵌入式系统的动态功耗,本文针对前人提出的用于预测空闲时间的指数平滑算法所存在的权重系数基于凑数法确定、算法难以适应大波动等问题,设计了一种新的权重系数,并构建了一种自适应的低功耗管理预测模型。当模型检测到预测的空闲时间超过工作和休眠状态切换的时间阈值后,中央处理器关停系统的部分外设,使系统进入低功耗休眠状态。仿真和单片机实验均表明,本文提出的低功耗管理预测模型能够有效地提高对处理器工作的空闲时间预测的准确性,且面对突发状况具有良好的自适应性,可显著降低嵌入式系统的功耗,有效延长设备的工作时间。

面向多虚拟电厂的分层分区多层互补动态聚合调控策略

虚拟电厂(virtual power plant,VPP)是一种新型运行模式,通过有效聚合电网中大量需求侧资源并制定有效的动态聚合调控策略,实现电网不同时空的功率互补,提高电网调控的灵活性和系统的经济性。从电网调度角度分析了典型电网需求响应行为特性,提出了需求响应能力指标和虚拟电厂分类聚合方法,构建了多源虚拟电厂调控模型,以其结果支撑虚拟电厂响应资源的分层分区互补调控。最后,以某园区为案例,分析了虚拟电厂调控策略的合理性和多源虚拟电厂调控的科学性。结果表明,整体动态调控策略可以引导虚拟电厂科学合理地发挥需求响应价值,促进电网负荷平稳和系统安全稳定运行。

Power Management of Parallel Hybrid Electric Power Train

This paper presents an integrated and detailed procedure to improve tile power management feature implemented in the integrated starter-generator （ISG） parallel hybrid electric vehicle. First, the configuration of the single-shaft ISG hybrid vehicle model established in MATLAB-Simulink environment is given. The vehicle model then is validated by comparing the experimental measurements and the simulation predictions of the traditional vehicle. The baseline rule based control strategy and the optimal control strategy using the dynamic programming （DP） algorithm are introduced. Finally, a suboptimal control strategy which employs the new control rules extracted from the optimal control strategy is designed with the remarkable fuel consumption performance.

基于机器学习的处理器电源管理方法研究

深入分析处理器电源管理方法后,将机器学习的思想应用到电源管理方法中。以树莓派4开发板为实验平台,比较传统动态调频调压技术与基于随机森林的动态调频调压技术在功耗和温度方面的性能表现。实验结果显示,基于随机森林的技术在不同负载下相较于传统技术表现出更为优越的功耗和温度控制特性。该技术在实验中取得显著的效果,为处理器电源管理领域的性能优化提供了新的视角和方法。

基于数据分析的电力工程动态智能监控技术研究

探讨了电力工程建设中动态智能监控技术的应用,研究这些技术在提升工程建设质量、效率和安全性方面的作用。首先,介绍了智能监控技术的核心元素,如实时数据采集和远程监控系统,并强调了它们在提升决策效率和精确性方面的重要性。随后,探讨了智能监控技术的核心组件,包括传感器、数据分析软件、监控界面及其协同工作的方式。特别关注了数据分析与智能决策支持系统在施工方法控制和环境因素控制方面的应用,突出了数据挖掘、模式识别和工程管理优化的重要性。最后,讨论了这些技术在实现更高效、更安全的电力工程建设中面临的挑战和未来的发展趋势。

发电企业风险管控4M屏障理论模型构建及其机制研究

为有效遏制事故发生,切实加强安全理论对电力安全生产指导和支撑作用,克服洋理论“水土不服”及现有的安全理论模型不能满足当前复杂电力安全生产实际需要的现状,构建本土特色电力安全生产管理理论及模型,基于国内外各类先进因果连锁、能量意外释放的事故致因及核电防护屏障、物、人、环、管(4M)屏障理论,结合国家能源集团发电企业安全生产现场客观实际及多年风险管控实践经验,把事故逆向思维(事故致因)和风险预控的正向思维(屏障控制)相结合,提出发电企业安全管理的物、人、环、管“4M屏障”理论,并通过动力学建模解析其内在事故演变根源和防范控制规律,梳理各要素内部逻辑关系。研究结果表明:该理论模型通过明确风险及其屏障控制对象、事故发展过程及其原因分析,能够为发电企业建立健全安全生产风险管理体系提供较为系统和全新的风险预控理念与方法论。

基于动态演化博弈论的电力需求侧管理策略研究

随着电力需求侧管理工作的不断推进,如何提升电网企业、电力用户等重要实施主体的参与积极性已成为当前亟待解决的问题。通过构建包含政府、电网企业和电力用户在内的动态演化博弈模型,分析不同博弈策略下三者之间的作用机制、成本收益及外部效应,讨论系统均衡点的稳定性情况。在此基础上通过仿真分析,验证动态演化博弈模型的科学性,结果表明:当系统中政府在兼顾自身成本与收益平衡的基础上增加激励力度、电网企业或电力用户的正外部效应增加时,各方的参与度会显著提升,达到电力需求侧管理稳态均衡的时间也会缩短。