基于改进理想点解法的电力变压器状态综合评估

变压器作为电力系统的枢纽设备,其运行状态直接影响系统的安全运行水平。因此,准确判断变压器的运行状态具有重要意义。目前对变压器状态综合评估的研究多是对单台变压器给出了状态评价等级,没有实现对电力企业多台变压器状态的综合排序。对多属性决策理论的理想点解法进行了研究,改进了效益型指标和成本型指标的规范化公式及正负理想解的定义,使之适用于变压器状态综合评估与状态排序,提出了基于理想点解法的变压器状态综合评估模型,为电力企业从整体上掌握多台变压器的状态,从而为变压器的检修决策提供依据。实例分析表明,提出的方法是合理有效的。     

火电厂建设项目影响评价及其环境因素分析

火电厂建设项目影响评价有利于控制项目风险.对火电厂建设项目影响评价中环境影响要素及指标进行了细致的分析,并对影响状况、相应处理措施及管理水平进行审查评估.利用TOPSIS法对火电机组环境影响状况进行实例评价,提出改进建议.     

一种结合模糊TOPSIS法和BP神经网络的变压器故障诊断方法

对变压器绝缘油采取色谱分析并进行故障诊断是目前变压器故障诊断的重要环节。针对传统故障诊断技术中降低状态检测与诊断系统的复杂度和增加系统采集的特征信息量的问题,首次提出将模糊TOPSIS 法用于状态检测信息处理。首先将TOPSIS 法在Vague集下进行扩展,把样本数据的多属性群问题转化为模糊多属性决策问题。然后计算Hausdorff矢量距离,得到可能解与理想解的相对贴近度,进行样本的优劣排序和聚类。在此基础上建立了简单的BP神经网络,实现了不同类型的模式识别。该方法可以快速、准确地判断变压器的运行状态,进而提高了变压器的安全、稳定及经济运行水平。     

通信建设项目评价模型研究

为提高项目决策的科学性和准确性,本文提出采用TOPSIS模型进行多目标决策,同时建立科学的指标体系和权重分配体系,解决项目多方案决策的难题。     

机组负荷分配的多目标优化和多属性决策

同时计及机组运行的经济性和污染排放,将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对火电厂负荷优化分配问题进行了研究。对于多目标优化问题,采用改进的非支配解排序的多目标遗传算法(NSGAⅡ),求出Pareto最优解,由这些Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算,然后用逼近理想解的排序方法(TOPSIS)进行多属性决策(MADM)研究,对Pa-reto最优解给出排序。给出了3台机组负荷分配的优化算例,计算表明所提方法适应性好,结果合理可行。     

江苏省水利投资结构合理度与水利投资类型相关性分析

基于江苏省水利建设1996～2010年投资分类统计数据,采用改进的TOPSIS法及灰色关联度分析法综合评价了江苏省水利投资结构。结果表明,1996～2010年江苏省水利投资结构合理度变化较大,1996～2003年较低,2004～2010年较高,在时间上呈波动上升趋势,总体上水利投资结构的合理性有明显的好转趋势。表明改进的TOPSIS法所得结果合理,适用于水利投资结构合理性的研究。     

Evaluating the Impact of Software Security Tactics: A Design Perspective

Design architecture is the edifice that strengthens the functionalities as well as the security of web applications. In order to facilitate architectural security from the web application’s design phase itself, practitioners are now adopting the novel mechanism of security tactics. With the intent to conduct a research from the perspective of security tactics, the present study employs a hybrid multi-criteria decision-making approach named fuzzy analytic hierarchy process-technique for order preference by similarity ideal solution (AHP-TOPSIS) method for selecting and assessing multi-criteria decisions. The adopted methodology is a blend of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity ideal solution (fuzzy TOPSIS). To establish the efficacy of this methodology, the results are obtained after the evaluation have been tested on fifteen different web application projects (Online Quiz competition, Entrance Test, and others) of the Babasaheb Bhimrao Ambedkar University, Lucknow, India. The tabulated outcomes demonstrate that the methodology of the Multi-Level Fuzzy Hybrid system is highly effective in providing accurate estimation for strengthening the security of web applications. The proposed study will help experts and developers in developing and managing security from any web application design phase for better accuracy and higher security.     

高校创新性人才评价决策系统的分析与设计

分析了高校创新性人才评价决策系统并介绍了方案的初步设计。在系统中,采取C/S模式构架,并采用功能强大、简单易学的Delphi6进行开发,数据库采用SQLServer2000,建立了不同于传统高校人才综合测评的新的侧重于创新性素质的评价指标体系。评价方法主要采用加权和法和TOPSIS法,并且使两者很好地结合起来,较好地实现对高校创新性人才的综合评价。     

产品技术经济综合评价原型系统

在层次分析法的基础上，提出了一种将TOPSIS法和AHP法进行融合的综合评价方法．此方法可对产品进行全面的技术经济综合评价，为企业进行决策提供依据，使产品研制建立在一个更加客观的量化分析基础之上．结果表明，这种方法科学合理、简明方便，能够处理多层复杂问题，为产品技术经济综合评价提供了思路，具有普遍意义，能推广应用于其他领域的评估．     

Black-Start Decision Making Based on Affinity Propagation and TOPSIS

Abstract

Finding the optimal black-start alternative plays an important role in speeding up the restoration process of a power system after a complete blackout. In this article, a clustering method, affinity propagation, is adopted to deal with the black-start decision making problem. A novel black-start decision making method based on affinity propagation and TOPSIS is proposed. The standard deviation method is used to calculate the weights of the indexes, and a weighted normalized decision matrix is constructed. Then affinity propagation is used to cluster the black-start schemes of the weighted normalized decision matrix, and the optimal cluster is determined. Finally, for each black-start scheme in the optimal cluster, the relative closeness value is computed according to the TOPSIS method, and the best black-start scheme is selected. Compared with the existing black-start decision making methods, the proposed method can not only rank the black-start schemes but also determine which grade each black-start scheme should belong to. Based on the data of an actual power system, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method is effective.