Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (6): 241-245.DOI: 10.3778/j.issn.1002-8331.1508-0177

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Correlation analysis and fusion algorithm of index in power grid planning

LIU Yangjun, WANG Qingxin, DING Jiaman   

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
  • Online:2017-03-15 Published:2017-05-11

电网规划指标的相关性分析及融合算法

刘洋均,王清心,丁家满   

  1. 昆明理工大学 信息工程与自动化学院,昆明 650500

Abstract: In the current decision-making indicator system, the correlation of indicators is often ignored which would affect the reliability and the economy of the entire grid planning. To solve the uncertainty of indexes decisions, it proposes the index correlation analysis and fusion algorithm based on the theory of the probability boxes. This paper firstly introduces the calculation of the Lucas correlation coefficient of each index. Then, by establishing the probability of box and using overlap factor, quantitative analysis can be made on the basis of making the fusion of the probability of box according to the correlating indicators. Using the underlying index of the power grid planning scheme of a provincial as an example to do an experiment, the result shows that the method is more timely and accurate to solve the problem of correlation and fusion compared with existing method.

Key words: probability boxes, Lucas model, fusion, overlap factor, power grid planning

摘要: 目前决策指标体系中,大多忽略了指标的相关性问题,这将影响整个电网规划的可靠性、经济性。针对指标决策中不确定性问题,提出一种基于概率盒理论的指标相关性分析模型及融合算法。首先对指标采用Lucas模型进行相关性分析;然后对相关性的指标建立概率盒,并根据相关系数进行概率盒融合;最后利用重叠因子进行定量分析。以某省电网规划方案的底层指标为例进行了实验,结果表明,该方法对解决相关性及其融合问题具有良好的实效性和精准度。

关键词: 概率盒, Lucas模型, 融合, 重叠因子, 电网规划