基于参数优化的超短期负荷预测调整策略

四川省电力公司调度控制中心目前已部署使用了四川并网发电厂实时调令系统,但该系统仅满足对发电单元负荷进行调整的需求,却无法实现计划曲线自动修正、对应联络线负荷变化调整实时出力的功能.本文通过对模型参数优化策略进行分析,提出了一套超短期负荷预测方案.     

Adaptive cross-layer MAC design for improved energy-efficiency in multi-channel wireless sensor networks

We present a novel cross-layer design for improving energy efficiency in a wireless sensor network that utilizes a multi-channel non-persistent CSMA MAC protocol with adaptive MQAM modulation at the physical layer. Cross-layer interactions are achieved through joint, traffic-dependent adaptation of the backoff probability at the MAC layer and the modulation order at the physical layer. The joint optimization of the backoff probability and the modulation order is conducted subject to a constraint on the packet retransmission delay. Such an optimization is shown to produce a significant improvement in the per-bit energy requirement for successful packet delivery. Our analytical findings are verified through numerical results and computer simulations.     

基于BP神经网络的食品价格的预测

针对目前食品价格上涨的问题,提出了一种基于神经网络的食品价格上涨的预测方法。借助于Matlab建立食品价格上涨数学模型。利用Matlab对各食品价格的数据进行分析,计算相应的食品价格,对结果进行对比。运用BP人工神经网络的多层神经网络对食品价格的实测值进行训练学习,建立模型。借助于此模型来对食品价格上涨进行预测和预报,以达到对食品价格预测预警的作用。应用实例表明：人工神经网络应用于食品价格的预测预警是比较理想的。     

配电变压器三相负荷自动调平技术方案探究

本文阐述配电变压器负荷三相不平衡带来的问题和隐患,并对常规的解决方法进行技术比较,在现有自动控制的基础上进行适当改良以达到有载调整三相负荷平衡的目的,有效的解决三相负荷不平衡给供电企业带来的困扰。     

电离层参数的相似日短期预测方法

提出了时间序列频率特征参数相似日和时间序列时频双特征参数相似日的电离层参数(F2层临界频率)短期预测方法,利用中国9个电离层观测台站一个太阳活动周(1977~1987年)的F2层临界频率数据对提前1~24h预测的精度进行了测试。结果表明:相对于现有的一些短期预测方法,相似日的预测精度有明显改善,该方法简便、有效,具有实际工程应用价值。     

基于萤火虫优化算法的不平衡网压下VSR自抗扰控制

传统控制方式下,三相VSR在电网电压不平衡时的网侧电流谐波较大,且其动态性能容易受到负载扰动的影响.针对这一问题,文中提出了一种基于萤火虫优化算法的自抗扰控制策略.在系统的电流内环加入一种与对象模型无关的自抗扰控制器对其进行闭环控制,及时对系统内外扰动进行主动补偿和抑制.同时,利用萤火虫优化算法对自抗扰控制器的关键参数...     

一种高效率反激同步整流DC-DC

目前,小型机载电子设备对二次电源体积、输入电压范围、输出电压种类、功率及效率的要求越来越高。基于单端反激拓扑和辅助绕组自驱动同步整流结构,设计了一种多路输出DC-DC电路。分析了多路电源的损耗组成、影响因素和解决措施,以指导电路设计和元器件选型,最终确定系统设计方案。重点对反激变压器设计、同步整流驱动设计和同步整流轻载损耗较大的原因开展分析和研究。实验电路测试结果表明,设计的反激同步整流DC-DC电路满足指标要求。     

Localized and load‐balanced clustering for energy saving in wireless sensor networks

In this paper, the ‘localized and load‐balanced clustering (LLBC)’ protocol is proposed for the energy savings and lifetime increases of wireless sensor networks. LLBC contains two approaches. One is improved cluster head rotation (ICHR) and the other is modified static clustering (MSC). ICHR uses the present cluster heads to select most energetic sensors as the next‐round cluster heads and avoids the margin cluster heads being selected as cluster heads repeatedly. MSC is suitable when the network has a few very high energetic sensors. It uses the method of inter‐cluster load balance to adjust the cardinality of each cluster as close to the average cardinality as possible. The simulation results with respect to FND (the time when a node dies first), HND (the time when half of the total nodes have died), and energy consumption show that the orders of effectiveness are: for ICHR and low‐energy adaptive clustering hierarchy (LEACH)‐C, before 250 rounds of cluster head rotations, there is no significant difference between the two, but after 250 rounds, ICHR>LEACH?C; and in general, LEACH?C>LEACH>MSC>mini variance>direct communication. Copyright © 2008 John Wiley & Sons, Ltd.     

Reconfiguration and Dynamic Load Balancing in Broadcast WDM Networks*

In optical WDM networks, an assignment of transceivers to channels implies an allocation of the bandwidth to the various network nodes. Intuition suggests, and our recent study has confirmed, that if the traffic load is not well balanced across the available channels, the result is poor network performance. Hence, the time-varying conditions expected in this type of environment call for mechanisms that periodically adjust the bandwidth allocation to ensure that each channel carries an almost equal share of the corresponding offered load. In this paper we study the problem of dynamic load balancing in broadcast WDM networks by retuning a subset of transceivers in response to changes in the overall traffic pattern. Assuming an existing wavelength assignment and some information regarding the new traffic demands, we present two approaches to obtaining a new wavelength assignment such that (a) the new traffic load is balanced across the channels, and (b) the number of transceivers that need to be retuned is minimized. The latter objective is motivated by the fact that tunable transceivers take a non-negligible amount of time to switch between wavelengths during which parts of the network are unavailable for normal operation. Furthermore, this variation in traffic is expected to take place over larger time scales (i.e., retuning will be a relatively infrequent event), making slowly tunable devices a cost effective solution. Our main contribution is a new approximation algorithm for the load balancing problem that provides for tradeoff selection, using a single parameter, between two conflicting goals, namely, the degree of load balancing and the number of transceivers that need to be retuned. This algorithm leads to a scalable approach to reconfiguring the network since, in addition to providing guarantees in terms of load balancing, the expected number of retunings scales with the number of channels, not the number of nodes in the network.     

GitHub中基于CNN-LSTM的开发者项目推荐模型

作为一个开源项目托管平台,GitHub以多开发者协同参与进行开源项目的开发,开发者作为GitHub的核心元素,保证了整个系统的活跃性,然而,很多新项目在短时间内无法找到合适的协同开发者而被拖延开发周期.针对这个问题,本文提出了一种基于Word2Vec的CNN-LSTM开发者项目推荐模型,该模型以Word2Vec训练开发者访问项目的序列,并将项目进行向量化表示,结合CNN-LSTM模型计算项目相似度并为开发者推荐合适的项目序列.通过提取GitHub中62,031个开发者在2015全年的项目访问数据进行项目预测和相似项目发现实验,实验结果表明,该模型推荐效果较佳,并且可以帮助开发者发现感兴趣的相似项目.