模块化多电平变换器全桥型子模块优化均压控制方法

模块化多电平变换器(MMC)子模块(SM)的数量与直流侧电压成正比,当SM增加时,会导致MMC的开关损耗急剧增加,因此降低功率器件的开关频率一直是MMC的重要研究方向之一。采用最近电平逼近调制(NLM)方式,提出一种基于全桥型SM的改进均压排序法,旨在降低MMC中功率器件IGBT的开关频率,该方法实现相对简单,无需额外的控制器,且易于扩展。最后,通过在MATLAB/Simulink平台搭建了19个全桥SM的仿真模型,验证了该方法的有效性。验证了所提全桥型SM优化均压策略,可以有效避免IGBT不必要的反复投切,降低IGBT的开关损耗,同时对外部输出特性不会产生负面影响。     

梯级水库群多目标优化调度规则研究——以溪洛渡-向家坝为例

为完整利用水库群多目标优化调度的非劣前沿信息,提出了一种优化调度函数参数、追求调度结果逼近非劣前沿的新型调度函数提取方法。对于水库群多目标优化调度模型,采用非支配排序多目标优化算法II（NSGA-II）求解,分别获取丰、平、枯典型年的非劣前沿集,将其作为代理模型的训练样本,实现二维非劣前沿的函数表达。采用多元线性构型构建调度函数,以调度结果逼近非劣前沿为目标,优化调度函数的参数项。该方法避免了多目标优化调度函数提取因决策破坏非劣前沿完整性,追求调度结果即为非劣解,在溪洛渡-向家坝水库群得到了验证,成功提取了针对非汛期发电、生态两目标的丰、平、枯典型年调度函数,并在相似来水年份下验证了结果的合理性。     

考虑多节点条件约束的柔直系统故障限流器与断路器优化配置方法

首先建立了适用于多节点柔直系统的潮流及故障电流的数值计算优化模型,并在三端仿真系统中验证了所建模型的准确性.随后设置约束条件及目标函数,利用粒子群优化算法建立了多端柔直系统中故障限流器与断路器的优化配置数学模型.在3节点环网中进行优化求解,并将结果配置到仿真模型检验其限流效果,验证了所提优化配置方法的准确性.最后通过所提多目标粒子群优化算法对11节点柔直系统进行故障限流器与断路器配置的优化求解,并将所得结果与多目标遗传算法进行对比.结果表明,所提优化配置方法具有较高的普适性,可实现多节点柔直系统中故障限流器与断路器的低成本配置.     

计及风电置信风险的源网协调多目标优化调度

为了更为合理灵活地评估风电高估/低估给电力系统优化调度带来的风险性以及降低调度决策的保守性,基于风电机会约束提出风电高估/低估置信风险功率偏差量化计算方法,并在决策变量中引入变压器变比调节和无功补偿容量优化,构建计及风电置信风险和源网协调运行的经济/风险多目标优化调度模型。提出一种基于可行性和非劣性综合排序回溯搜索算法,从而实现对调度模型的高效准确求解。IEEE 30节点系统算例结果验证了所提方法的有效性和优越性。     

基于可拓距K-均值聚类和正弦微分进化算法的风储联合系统优化配置

以最大化风储联合投资商的收益和风电就地消纳率为目标,考虑源网荷协同优化和需求响应构建了风储联合系统的多目标优化配置模型;采用可拓距K-均值聚类算法对分布式风电出力和负荷需求的不确定性进行多场景分析,以实现更为准确而均衡的场景缩减;通过引入多核并行运行环境与正弦函数的思想,提出基于并行多目标正弦微分进化算法对优化配置模型进行高效求解;以IEEE 33节点配电系统为算例进行风储联合系统的优化配置,仿真结果验证了所建模型的有效性和优越性.     

基于时空特性以及需求响应的DG和EV充电站多目标优化配置

针对可再生分布式电源(DG)及电动汽车(EV)大规模接入给配电网带来的用电量增长以及电压波动问题,提出一种基于时空特性以及需求响应的DG和EV充电站多目标协调优化配置方法.通过提取城市路网的拓扑结构,监测路网流量,基于交通规划软件TransCAD进行起讫点(OD)矩阵反推,构建出行概率矩阵以描述用户的出行特性;基于蒙特卡洛方法模拟EV的时空分布特性,考虑EV、DG与常规负荷的时序特性,并基于改进K-means算法构建风-光-负荷的典型运行场景;兼顾电网侧与用户侧,以综合效益、系统负荷波动以及充电耗时成本为目标,构建DG和EV充电站的多目标联合配置模型,并采用改进粒子群优化算法进行求解.结合IEEE 33节点配电网与某城区主干道路网模型进行仿真分析,结果验证了所建模型的有效性与可行性.     

流程生产的可视化整合和多目标优化软件

针对现代企业所面临的挑战，开发了一个软件系统可以根据市场的动态变化和企业内部的设备状况，优化设备配置、优化原料和产品方案。采用可视化形式实施仿真运行和经济效益分析预测。     

A demerit-fuzzy rating system,monitoring scheme and classification for manufacturing processes

For monitoring online manufacturing processes, the proportion of weights imposed on each type of product’s defects (nonconformities or demerits) has a profoundly effective impact on control charts’ performance. Apparently, the demerit-chart approach is superior than the widely-used c-chart scheme, because it allows us to place relative precise weights (real numbers) on defects according to their distinctly inferior degrees affecting the product quality so that the abnormal variations of processes can be literally exposed. However, in many applications, the seriousness of defects is evaluated partially or entirely by the inspectors’ perceptive judgement or knowledge, so with the precise-weight assignment, the demerit rating mechanism is considered to be somewhat constrained and subjective which inevitably leads to the targeted manufacturing process with limited and possibly biased information for online surveillance. To cope with the drawback, a demerit-fuzzy rating system and monitoring scheme is proposed in this paper. We first incorporate fuzzy weights (fuzzy numbers) to properly reflect the severity measures of defects which are categorized linguistically. Then, based on properties of fuzzy set theory and proposed approaches for fuzzy-number ranking, we develop the demerit-fuzzy charting scheme which is capable of discriminating process conditions into multi-intermittent statuses between in-control and out-of-control. This approach improves the traditional process control techniques with the binary-classification restraint for the process conditions. Finally, the proposed demerit-fuzzy rating system, monitoring scheme, and classification is elucidated by an application in garment industry to monitor textile-stitching nonconformities conditions.     

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.