适合于分析广义负荷序列间相关关系的最优延位法

发布时间：2018-04-13 08:46

本文选题：相关性分析 + 最优延位法　；参考：《电力系统自动化》2017年21期

【摘要】：传统的相关性分析方法局限于获得明显的相关关系,难以挖掘序列之间潜在的相关性信息,使得电力系统调度运行的参考信息受损。针对此问题,提出一种用于分析广义负荷序列之间潜在相关关系的最优延位法,该方法通过对序列进行适当延位,挖掘出广义负荷序列之间的间接相关关系。首先,以获得序列间最大Pearson相关系数为目标函数,将位移范围与序列数据的时间单调性作为约束条件,建立最优延位模型;然后,提出了模型的解算策略;最后,以德国2016年区域数据为例,分别对单日、每月和全年数据的相关关系进行分析计算,统计得到最大相关系数与需要的位移时间。分析结果表明,与传统的相关性分析方法相比,所提最优延位法可以发现序列之间潜在的延位相关关系,完善了风电、光伏和负荷等广义负荷序列之间的相关性分析方法。
[Abstract]:The traditional correlation analysis method is limited to obtaining the obvious correlation relation, and it is difficult to mine the potential correlation information between the sequences, which makes the reference information of power system dispatching and operation damaged.In order to solve this problem, an optimal continuation method is proposed to analyze the potential correlation between generalized load sequences.Firstly, taking the maximum Pearson correlation coefficient between sequences as the objective function, taking the monotonicity of the displacement range and the sequence data as the constraint condition, the optimal continuation model is established. Then, the solution strategy of the model is proposed.Taking the regional data of Germany in 2016 as an example, the correlation between the data of one day, a month and a whole year is analyzed and calculated, and the maximum correlation coefficient and the required displacement time are obtained by statistics.The results show that, compared with the traditional correlation analysis method, the proposed optimal continuation method can find the potential correlation relationship between sequences, and improve the correlation analysis method between wind power, photovoltaic and load generalized load sequences.
【作者单位】：广西大学广西电力系统最优化与节能技术重点实验室;
【基金】：国家重点研发计划资助项目(2016YFB0900100) 国家自然科学基金资助项目(51377027)~~
【分类号】：TM732;TM74

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