Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Wind power has emerged as the most promising option for providing sustainable eco-friendly power supply to the modern world. Due to its unpredictable nature, integration of wind power into the conventional power grid is a very challenging task having dynamic characteristics. Due to the inherent uncertainty associated with wind availability, additional spinning reserve needs to be scheduled in order to maintain security and supply reliability. Multi-period multi-objective optimal dispatch (MPMOOD) is presented for wind integrated power system with reserve constraints. The complex relationship between wind power availability, spinning reserve allocation and their impact on economic/environmental cost are analysed using an elaborate model.A new multi-objective Series PSO-DE (SPSO-DE) hybrid algorithm is proposed where the two paradigms, differential evolution (DE) and particle swarm optimization (PSO) share domain information and maintain a synergistic cooperation to overcome their individual weaknesses. For multi-objective (MO) problems, the selection operation in SPSO-DE is replaced by a 5-class time-varying fuzzy selection mechanism (TVFSM) to avoid saturation and to increase Pareto diversity. To promote convergence towards the central part of the Pareto front and to quickly isolate the boundary solutions, Guassian membership function is employed. Elitism is applied to preserve good solutions and momentum operation is used to stop premature convergence. The proposed method expedites the search for the best solution, i.e. the solution which satisfies all the objectives of the MO problems. To test the performance and computational efficiency, the proposed method is applied on two standard test power systems.     

基于多目标粒子群算法的电力系统环境经济调度研究

提出了一种新的多目标粒子群优化(Multi-Objective Particle Swarm Optimization, MOPSO )算法,用于求解电力系统的环境/经济调度问题。通过设计特定的约束修正因子,将不可行解修正成可行解,并在此基础上用惩罚函数法构建了新的适用于多目标粒子群的适应度函数模型。根据帕累托占优条件形成历史帕累托最优解集和全局帕累托最优解集,引入稀疏度排序法选择全局最优解,基于帕累托最优前沿的斜率特性,提出用斜率法筛选非劣解,采用基于模糊数学的满意度评价模型选择POF的折衷最优解。最后,用IEEE-30节点标准测试系统对所提算法进行了仿真测试,并与其他算法进行了对比。仿真结果表明所提算法可行、有效。     

COMPROMISE: AN EFFECTIVE APPROACH FOR CONDITION-BASED MAINTENANCE MANAGEMENT OF GAS TURBINES

A decision-based approach to condition-based maintenance management of rotating machinery is introduced and illustrated by formulating and solving a multiple objective maintenance management problem for a 15 MW industrial gas turbine. The compromise Decision Support Problem approach is used because it provides a convenient way of incorporating both information from condition monitoring and considerations of factors such as machine degradation, operating cost (fuel cost), production loss, maintenance cost, environmental protection, machine availability, etc. The focus in this paper is on explaining the approach rather than on the results per se.     

“U”型结构多目标拓扑优化

采用变密度法和折衷规划法建立了结构的多目标拓扑优化模型,并利用分析层级法确定了子目标权重。以"U"型结构为对象进行了多目标拓扑优化设计,得到了结构质量较轻的新结构形式。该方法可应用于结构的优化设计。     

Improvement of McCullagh-Barreto key agreement with KCI-security

McCullagh-Barreto key agreement protocol and its variant achieve perfect forward security and key generation center (KGC) forward security, but provide no resistance to key compromise impersonation attack (KCI attack). In this paper, we give a formal treatment of key compromise impersonation (KCI) attack and define the security notion against it. Then an variant of McCullagh-Barreto protocol is presented with only one more Hash operation. The improved protocol preserves perfect forward security and KGC forward security, and furthermore is proved to be secure against KCI attack under k-Gap-BCAA1 assumption.     

折中规划分类性能的少数类误分代价优化设计

针对代价敏感思想在类不平衡问题中的传统代价给定方式,提出了分类性能需求引导代价优化的因子量化方法。分类性能需求表示为相关于代价因子[c]的正负类分类性能指标函数式,为代价择优标准。应用遗传算法基于该标准在指定值域内寻优,得到最优代价因子,并将其代入代价敏感Boosting学习方法,产生基于给定分类性能的分类模型。折中分类性能的算法实现以正负类召回率的几何平均作为择优标准,选用了四类算法（基算法C4.5和ZeroR）依次在三组样本集上进行分类建模。与传统代价给定方式代入算法相比,寻优过程确定的代价因子代入AdaCost算法后,基于C4.5和ZeroR的分类器在TP与TN上的变化幅度依次为33.3%~200%、[-49%~-15.6%]和[-44.4%~-16.7%、]25%~400%。前者改善了正类误判情形,且未造成负类误判严重化;后者改善了负类严重误判情形,且正类召回率保持在0.5以上,分类性能达到较为均衡的状态。     

基于工况风险评估的叉车门架多工况拓扑优化

以某3T型内燃叉车的标准门架为例,实现了典型工况下门架系统的整体非线性有限元分析;基于工况数据（工作时长、结构应力及形变位移）,提出了工况风险评估方法及工况风险指标（RI）的概念;依据工况风险指标,提出了一种分配多工况拓扑优化权重系数的方法;采用折中规划法,以多工况下门架加权柔度最小为目标函数对门架结构进行拓扑优化;根据优化结果对门架结构进行改进设计,并进行了有限元分析。研究结果表明：风险评估法较传统平均分配法能得到更优的目标值;与原门架结构相比,新门架结构在3种典型工况下的最大应力分别减小10.05%、10.25%和1.58%,最大位移分别减小13.17%、12.93%和8.28%,质量减小7 kg。     

基于相容决策支持问题法的稳健优化设计方法

稳健优化设计本质上是多目标的优化问题,相容决策支持问题法是一种有效的多目标优化设计方法,其实质是一种包含数学规划和目标规划的混合方法。将相容决策支持问题法应用到稳健优化设计中,建立了稳健优化设计的相容决策支持问题法模型。通过对起重机变幅机构的补偿滑轮组系统的稳健优化设计,给出了具体的分析求解过程。实例表明,相容决策支持问题法对解决工程多目标优化设计的问题是有效的。     

一种检测被捕获节点的基于异常的算法研究

无线传感器网络在许多应用场合里需要采集较敏感的数据,因此安全问题至关重要。一旦传感器节点被捕获,且没有采取相应措施,节点的密钥信息易被泄露,攻击者完全可伪装成这些节点,向网络任意注入错误的信息,由此导致网络的安全性能急剧下降。提出了针对被捕获节点的一种基于异常的入侵检测算法,能有效识别无线传感器网络的被捕获节点。算法对传感器节点间关系进行抽象,采用传感器网络的事件驱动特性来确定某节点在固定时间间隔内是否在发生数据包,基站通过检测可疑节点的数据包发送时间的差异来加以确认。算法不依赖于任何被捕获节点如何行动和密谋的假设,能识别出偏离正常行为值的最大多数被捕获节点,而不会出现"假肯定"。     

分子动力学模拟研究盐离子对十二烷基硫酸钠胶束溶液中亲水亲油协调机制的影响

The presence of salt has a profound effect on the size, shape and structure of sodium dodecyl sulfate (SDS) micelles. There have been a great number of experiments on SDS micelles in the presence and absence of salt to study this complex problem. Unfortunately, it is not clear yet how electrolyte ions influence the structure of micelles. By describing the compromise between dominant mechanisms, a simplified atomic model of SDS in presence of salt has been developed and the molecular dynamics (MD) simulations of two series of systems with different concentrations of salt and charges of ion have been performed. Polydispersity of micelle size is founded at relatively high concentration of SDS and low charge of cation. Although the counter-ion pairs with head groups are formed, the transition of micelle shape is not observed because the charge of cation is not enough to neutralize the polar of micelle surface.