本文選題：電力仿真　切入點(diǎn)：高維數(shù)據(jù)　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)代電網(wǎng)的規(guī)模不斷擴(kuò)大、電壓等級(jí)不斷升高,仿真分析成為現(xiàn)代電網(wǎng)研究的重要手段。然而,傳統(tǒng)分析方法在面對(duì)大電網(wǎng)的海量仿真數(shù)據(jù)時(shí)缺乏有效的處理工具和手段,難以幫助分析人員快速掌握電網(wǎng)運(yùn)行信息、深入研究系統(tǒng)變化規(guī)律。本文圍繞電力系統(tǒng)仿真分析中的高維時(shí)序數(shù)據(jù),通過(guò)可視分析技術(shù),提出了一種空間分布地理圖與時(shí)變曲線圖協(xié)同分析的關(guān)聯(lián)分析方法�？臻g分布地理圖對(duì)高維仿真數(shù)據(jù)進(jìn)行基于地理分布的分層可視分析,時(shí)變曲線圖對(duì)大規(guī)模時(shí)序數(shù)據(jù)進(jìn)行模式分析;通過(guò)時(shí)間和物理變量的關(guān)聯(lián)對(duì)兩個(gè)視圖進(jìn)行協(xié)同分析,允許分析人員利用交互工具觀察兩種視圖的協(xié)同變化。該分析方法將領(lǐng)域?qū)＜椰F(xiàn)有的專業(yè)經(jīng)驗(yàn)知識(shí)與電網(wǎng)仿真數(shù)據(jù)更好地結(jié)合,對(duì)現(xiàn)有電網(wǎng)仿真可視化工具的不足進(jìn)行了有效補(bǔ)充。論文主要貢獻(xiàn)包含以下三方面:1.針對(duì)空間分布地理圖提出了一種分層可視分析的方法,將不同物理量分別置于不同圖層,以便在同一視圖中同時(shí)展示多維數(shù)據(jù),展示不同變量間的關(guān)聯(lián)關(guān)系;2.實(shí)現(xiàn)了基于核密度函數(shù)的高維曲線可視分析方法,通過(guò)提取高維曲線的特征對(duì)其分類并映射到二維平面,從而允許分析人員同時(shí)觀察上千條曲線的變化與分布形態(tài);3.提出了一種地理圖與曲線圖協(xié)同分析的關(guān)聯(lián)分析方法,允許分析人員關(guān)聯(lián)分析同一電力物理量數(shù)據(jù)的時(shí)空特征以及不同物理量數(shù)據(jù)的關(guān)聯(lián)關(guān)系。本文使用多個(gè)案例驗(yàn)證了提出方法的實(shí)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.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TM743

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本文編號(hào)：1632169