基于电力系统仿真数据的可视分析

发布时间：2018-03-19 01:16

本文选题：电力仿真　切入点：高维数据　出处：《浙江大学》2017年硕士论文　论文类型：学位论文

【摘要】：现代电网的规模不断扩大、电压等级不断升高,仿真分析成为现代电网研究的重要手段。然而,传统分析方法在面对大电网的海量仿真数据时缺乏有效的处理工具和手段,难以帮助分析人员快速掌握电网运行信息、深入研究系统变化规律。本文围绕电力系统仿真分析中的高维时序数据,通过可视分析技术,提出了一种空间分布地理图与时变曲线图协同分析的关联分析方法。空间分布地理图对高维仿真数据进行基于地理分布的分层可视分析,时变曲线图对大规模时序数据进行模式分析;通过时间和物理变量的关联对两个视图进行协同分析,允许分析人员利用交互工具观察两种视图的协同变化。该分析方法将领域专家现有的专业经验知识与电网仿真数据更好地结合,对现有电网仿真可视化工具的不足进行了有效补充。论文主要贡献包含以下三方面:1.针对空间分布地理图提出了一种分层可视分析的方法,将不同物理量分别置于不同图层,以便在同一视图中同时展示多维数据,展示不同变量间的关联关系;2.实现了基于核密度函数的高维曲线可视分析方法,通过提取高维曲线的特征对其分类并映射到二维平面,从而允许分析人员同时观察上千条曲线的变化与分布形态;3.提出了一种地理图与曲线图协同分析的关联分析方法,允许分析人员关联分析同一电力物理量数据的时空特征以及不同物理量数据的关联关系。本文使用多个案例验证了提出方法的实用性。
[Abstract]:The scale of modern power grid is expanding and the voltage level is increasing. Simulation analysis has become an important means of modern power grid research. However, the traditional analysis method is lack of effective processing tools and means in the face of massive simulation data of large power grid. It is difficult to help the analysts to grasp the power network operation information quickly and to study the system change law deeply. This paper focuses on the high-dimensional time series data in the power system simulation and analysis, through the visual analysis technology, This paper presents an association analysis method for collaborative analysis of spatially distributed geographic maps and time-varying curves. Spatial distributed geographic maps are used for layered visual analysis of high-dimensional simulation data based on geographical distribution. The time-varying graph is used to analyze the large-scale temporal data, and the time and physical variables are associated to analyze the two views. Allows analysts to use interactive tools to observe collaborative changes between the two views. This method combines the expertise of domain experts with grid simulation data better. In this paper, the main contributions include the following three aspects: 1. A layered visual analysis method for spatial distribution geographic maps is proposed, in which different physical quantities are placed in different layers. In order to display multidimensional data in the same view at the same time and to show the correlation relationship among different variables. 2. A visual analysis method of high-dimensional curve based on kernel density function is implemented, which can be classified and mapped to two-dimensional plane by extracting the features of high-dimensional curve. This allows analysts to observe the changes and distribution patterns of thousands of curves at the same time. 3. An association analysis method for collaborative analysis of geographical maps and curves is proposed. The temporal and spatial characteristics of the same electric power physical quantity data and the correlation relation of different physical quantity data are allowed to be analyzed by the authors. The practicability of the proposed method is verified by using multiple cases in this paper.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TM743

【参考文献】