Smart dispatching and metaheuristic swarm flow algorithm

The resource saving dispatching aims at finding the optimal combination of powers produced by power generating units that minimizes the total costs subject to given constraints. A metaheuristic swarm flow algorithm is proposed. Results of the comparative analysis of the efficiency of this algorithm on benchmark problems are presented. The comparison was performed with the particle swarm optimization, genetic, and biogeography-based optimization algorithms using systems consisting of 6 and 20 power generating units as examples. The flow algorithm converges to the optimal solution using less computational resources.     

基于大数据技术的小水电机组负荷分配优化平台研究及应用

为提高水能利用率,解决小水电机组以最优效率为目标负荷分配问题,开发了一套基于大数据技术的小水电机组负荷优化分配平台。平台通过对基础数据和在线测量数据的分析,自动识别机组的状态条件、运行工况、稳定性能等情况。在此基础上,给出一种在线基于实时动态自校正的逐次迭代效率曲面拟合方法,解决大数据样本误差噪声引起的效率拟合误差以及机组的运行状态改变后效率变化引起的误差问题,实现了在线效率曲面的自动实时动态绘制。给出了基于机组综合稳定指标及临界振动区的小水电机组最优有功分配方法,解决了机组在最优有功分配负荷时机组的安全稳定运行问题,对多机组最优效率进行协调控制,最终实现小水电机组的节能增效。经工程实际应用,验证了技术的可行性。     

An adaptive optimization system and point tracking technique for estimation of variable-speed seawater pumped storage

There are few kinds of researches on the capacity optimization of seawater pumped storage with variable-speed units. First, the pumped storage effects are investigated to smoothing the large-scale offshore wind power. From the perspective of energy saving and improving efficiency, a method on maximum efficiency tracking is proposed, based on the synthetic characteristic curve of the pump turbine. Second, under the background of the time-of-use electricity price in the power market, the relevant model is established, taking full account of the characteristics of random seasonal fluctuation of tailwater level. Besides, this model satisfies the constraints of variable-speed units (especially under the pump mode). Finally, the key factors influencing profit growth, such as the fluctuation rate, the unit type, and the optimal capacity, are investigated based on a real case. The results show that the variable-speed unit can give full play to the efficiency advantage when the operating head and power demand of the plant change sharply. Because the output range of the variable-speed unit is extensive and can be continuously adjusted (especially in pump mode), the joint system will actively curtail the offshore wind to seek the maximum profit.

     

无线传感器网络中基于簇结构的公钥认证策略

在无线传感器网络中使用hsh算法取代传统的公钥加密算法(PKC),提出了基于能量层和位置关系分簇算法的公钥认证策略(SPABEP),该策略可以有效地减少节点内存空间的消耗.在对SPABEP进行改进的基础上,又提出了基于部署信息的簇结构公钥认证策略(SPABDK),SPABDK更好地优化了节点内存空间消耗与通信负载的关系.仿真结果表明所提出的基于簇结构的公钥认证策略比传统的公钥认证策略具有更好的节能性能.     

一种基于能量预测的双簇首路由控制算法

在深入研究经典网络路由算法LEACH的基础上,提出了一种基于能量预测的双簇首(DCHBEF)路由控制算法。该算法利用改进的阈值产生式与节点剩余能量相关性并设计主、次簇首的选举和工作机制,解决了LEACH和SAHRC协议中的盲目分簇和簇内单簇首通信方式缺陷。仿真结果表明:DCHBEF算法比原有的LEACH和SAHRC算法有较好的节能性,并提高全网通信效率。     

SUDBC:一种基于空间单元密度的快速聚类算法

随着数据规模越来越大,要求聚类算法有很高的执行效率,很好的扩展性,能发现任意形状的聚类以及对噪音数据的不敏感性.提出了一种基于空间单元密度的快速聚类算法SUDBC,该算法首先将被聚类的数据划分成若干个空间单元,然后基于空间单元密度将密度超过给定阈值的邻居单元合并为一个类.实验结果验证了SUDBC算法具有处理任意形状的数据和对噪音数据不敏感的特点.     

An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs’ homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.     

Adaptive clustering in wireless sensor networks by mining sensor energy data

Clustering has been well received as one of the effective solutions to enhance energy efficiency and scalability of large-scale wireless sensor networks. The goal of clustering is to identify a subset of nodes in a wireless sensor network, then all the other nodes communicate with the network sink via these selected nodes. However, many current clustering algorithms are tightly coupled with exact sensor locations derived through either triangulation methods or extra hardware such as GPS equipment. However, in practice, it is very difficult to know sensor location coordinates accurately due to various factors such as random deployment and low-power, low-cost sensing devices. Therefore, how to develop an adaptive clustering algorithm without relying on exact sensor location information is a very important yet challenging problem. In this paper, we try to address this problem by proposing a new adaptive clustering algorithm for energy efficiency of wireless sensor networks. Compared with other work having been done in this area, our proposed adaptive clustering algorithm is original because of its capability to infer the location information by mining wireless sensor energy data. Furthermore, based on the inferred location information and the remaining (residual) energy level of each node, the proposed clustering algorithm will dynamically change cluster heads for energy efficacy. Simulation results show that the proposed adaptive clustering algorithm is efficient and effective for energy saving in wireless sensor networks.     

连续状态自适应离散化基于K-均值聚类的强化学习方法

使用聚类算法对连续状态空间进行自适应离散化．得到了基于K-均值聚类的强化学习方法．该方法的学习过程分为两部分：对连续状态空间进行自适应离散化的状态空间学习，使用K-均值聚类算法；寻找最优策略的策略学习．使用替代合适迹Sarsa学习算法．对连续状态的强化学习基准问题进行仿真实验，结果表明该方法能实现对连续状态空间的自适应离散化，并最终学习到最优策略．与基于CMAC网络的强化学习方法进行比较．结果表明该方法具有节省存储空间和缩短计算时间的优点．     

基于分簇协作频谱感知的认知无线网能量效率研究

针对认知无线网能量消耗引起的能量效率较低问题,在研究认知无线网分簇协作频谱感知能量效率的基础上,提出了一种最优功率分配算法来最大化次用户系统的能量效率。通过建立基于次用户能量效率最大化传输优化模型,在考虑传输功率、感知时间以及干扰限制的情况下,利用拉格朗日函数及KKT条件,得到最优次用户发射功率分配算法,以达到系统能量效率最大化的目的,同时分析了不同参数对能量效率的影响。仿真结果表明,本文所提算法能有效提高次用户系统的能量效率、减少系统开销。     

基于分簇的无线传感器网络节能路由算法研究与应用探讨简

随着科技进步和经济的进一步发展,无线网络信息技术逐渐成为网络通信技术的重要组成部分。为了适应时代要求,节能环保,提高无线传感器网络路由的工作效率,进行基于分簇的无线传感器网络节能路由算法研究与应用探讨就显得尤为重要。本文基于现代信息技术高速发展的条件下基于分簇的无线传感器网络节能路由新算法的研究背景,从人文社会学的角度对基于分簇的无线传感器网络节能路由算法的现状、存在的问题及产生原因进行了分析,并在进行基于分簇的无线传感器网络节能路由算法研究与应用探讨的必要性探讨的基础上,提出了加强基于分簇的无线传感器网络节能路由算法研究与应用探讨的具体对策,为创新无线网络传感器路由算法,实现无线路由的节能高效,促进无线网络路由技术的向前发展,提供了一些切实可行的指导性意见,为其指明了前行的正确方向。     

一种基于地理位置信息的高能效无线传感器网络

分簇协议的目的是要利用节点有限的资源, 完成高效的数据采集任务, 延长网络使用寿命. 在基于簇的网络结构基础上, 提出并行分布式自组织成簇协议 (Parallel distributed self-organization clustering protocol, PDSOCP), 按地理位置将目标区域划分为若干均匀分布的逻辑区域, 每个区域根据节点剩余能量多少以及到区域内其余节点平均距离远近选择一个簇头, 以代理和簇头共同管理簇事务, 并建立了自适应路由. 实验表明, 该算法能够减少网络能耗, 有效延长网络寿命, 获得更好的性能.     

An ICA based approach for solving profit based unit commitment problem market

With the advent of paralleling and implementation of restructuring in the power market, some routine rules and patterns of traditional market should be accomplished in a way different from the past. To this end, the unit commitment (UC) scheduling that has once been aimed at minimizing operating costs in an integrated power market, is metamorphosed to profit based unit commitment (PBUC) by adopting a new schema, in which generation companies (GENCOs) have a common tendency to maximize their own profit. In this paper, a novel optimization technique called imperialist competitive algorithm (ICA) as well as an improved version of this evolutionary algorithm are employed for solving the PBUC problem. Moreover, traditional binary approach of coding of initial solutions is replaced with an improved integer based coding method in order to reduce computational complexity and subsequently ameliorate convergence procedure of the proposed method. Then, a sub-ICA algorithm is proposed to obtain optimal generation power of thermal units. Simulation results validate effectiveness and applicability of the proposed method on two scenarios: (a) a set of unimodal and multimodal standard benchmark functions, (b) two GENCOs consist of 10 and 100 generating units.     

高斯分布无线传感器网络簇头选择算法

针对无线传感器网络的能量受限问题,通常采用分簇聚合减少数据传输来实现能量节省。提出了一种基于高斯分布的分簇网络模型的簇头选择算法,构建了簇头选择函数的概率模型。模型以信道增益、剩余能量、簇内节点间距离及节点位置为参数,通过比较节点的概率函数值选择簇头。结果表明该选择算法能较大程度地提高网络能耗效率、延长网络生存时间。     

一种基于功率控制的WSN分簇路由算法研究

针对无线传感器网络研究领域中能量均衡性难以控制的问题,在LEACH协议的基础上提出了一种功率控制的分簇路由算法(PCRA算法).该算法基于最优连通功率,同时考虑节点的剩余能量、相对距离以及最优的邻居节集合对簇头的选择机制进行优化,从而实现网络稳定成簇和数据传输方式上的优化.通过结合距离、路径损耗和剩余能量等因数,使用权值来选择下一跳节点,实现了簇间通信,有效地避免了分簇协议应用被网络区域大小局限的问题.仿真数据显示,PCRA算法可使全网节点间的冲突区间变少,降低节点之间的竞争强度,进一步提高网络能量的有效性和吞吐量.     

认知Ad hoc网络的频谱接入跨层设计方案*

为提高分布式认知Ad hoc网络的频谱感知精准性和能量有效性,提出了一种基于能量效率的频谱接入跨层设计方案。首先通过基于感知因子的完全二分图分解法对网络进行分簇,进而在簇内联合优化次用户的发射功率和接入概率,最终得到最优信道接入和功率分配方案。仿真结果表明,与传统算法相比该方案可通过合理分簇提高分布式认知网络的感知精度,在保证感知性能的条件下使系统能量效率得到有效提升。     

Data Envelopment Analysis of clinics with sparse data: Fuzzy clustering approach

This paper presents a method for utilizing Data Envelopment Analysis (DEA) with sparse input and output data using fuzzy clustering concepts. DEA, a methodology to assess relative technical efficiency of production units is susceptible to missing data, thus, creating a need to supplement sparse data in a reliable and accurate manner. The approach presented is based on a modified fuzzy c-means clustering using optimal completion strategy (OCS) algorithm. This particular algorithm is sensitive to the initial values chosen to substitute missing values and also to the selected number of clusters. Therefore, this paper proposes an approach to estimate the missing values using the OCS algorithm, while considering the issue of initial values and cluster size. This approach is demonstrated on a real and complete dataset of 22 rural clinics in the State of Kansas, assuming varying levels of missing data. Results show the effect of the clustering based approach on the data recovered considering the amount and type of missing data. Moreover, the paper shows the effect that the recovered data has on the DEA scores.     

无线传感器网络中基于对策论的功率控制

运用对策论中的Stackelberg策略,提出了基于TDMA-CDMA的分簇结构无线传感器网络（WSN）的反向链路功率控制的新算法。根据TDMA机制和CDMA机制相结合的分簇结构无线传感器网络的基本特点,以网络中的簇为单位,建立了一个该簇簇头采用Stackelberg策略控制节点发射功率以达到最大化能量有效性并兼顾网络寿命的数学模型。仿真结果表明Stackelberg策略能起到控制功率及激励网络优化的作用。     

基于密度峰值的网格聚类算法

2014年提出的密度峰值聚类算法,思想简洁新颖,所需参数少,不需要进行迭代求解,而且具有可扩展性。基于密度峰值聚类算法提出了一种网格聚类算法,能够高效地对大规模数据进行处理。首先,将N维空间粒化为不相交的长方形网格单元;然后,统计单元空间的信息,利用密度峰值聚类寻找中心点的思想确定中心单元,即中心网格单元被一些低局部密度的数据单元包围,而且与比自身局部密度高的网格单元的距离相对较大;最后,合并与中心网格单元相近网格单元,从而得出聚类结果。在UCI人工数据集上的仿真实验结果表明,所提算法能够较快得出聚类中心,有效处理大规模数据的聚类问题,具有较高的效率,与原始的密度峰值聚类算法相比,在不同数据集上时间损耗降低至原来的1/100~1/10,而精度损失维持在5%~8%。     

基于协作MIMO的多跳WSN动态分簇选择算法研究

为解决基于协作多输入多输出(Multi-input multi-output, MIMO)的同构无线传感器网络(Wireless sensor networks, WSN)能量节省与能耗均衡问题, 建立了多跳分布式WSN系统模型. 对协作MIMO通信中的簇间长传输距离与簇内短传输距离进行了分析, 找到与传统单输入单输出(Single-input single-output, SISO)传输相比更节省能量的距离门限. 根据分析提出了一种新的基于剩余能量与距离门限的动态分簇(Dynamic clustering based on remaining energy and distance thresholds, DCREDT)选择算法, 在节省能量的前提下, 使剩余能量较大的节点优先成为簇首, 实现了簇首与其他节点之间的能耗均衡. 最后分析了采用DCREDT选择算法进行多跳传输的总能耗, 并仿真验证了该算法的合理性与有效性.