本文選題：數(shù)據(jù)挖掘技術 + 風電　； 參考：《山東大學》2015年碩士論文

【摘要】：風電已經(jīng)成為發(fā)展最迅速、技術最為成熟的新型清潔能源,我國非常重視風電的發(fā)展,目前風電裝機容量已經(jīng)達到了世界第一位。但是由于風電出力具有很強的隨機性和波動性,風電的大規(guī)模并網(wǎng)對電力系統(tǒng)調(diào)度運行的調(diào)頻、調(diào)峰、調(diào)度等多個方面產(chǎn)生了很大影響,增加了電網(wǎng)調(diào)度運行的難度。因此,只有從多個時間和空間尺度對風電出力進行分析,全面掌握風電的出力特性,才能為風電并網(wǎng)后電力系統(tǒng)各個方面的分析研究提供參考和關鍵數(shù)據(jù)支撐,更好地進行風電大規(guī)模并網(wǎng)消納。本文針對現(xiàn)有數(shù)據(jù)挖掘和特性信息提取技術在處理海量風電出力數(shù)據(jù)提取風電出力特性時的缺點和不足進行了補充和完善,主要研究了數(shù)據(jù)歸約、數(shù)據(jù)聚類、數(shù)據(jù)統(tǒng)計、相關性分析四個方面的數(shù)據(jù)挖掘和特性信息提取技術,從而建立了較為完整的應用在風電出力特性分析中的海量數(shù)據(jù)數(shù)據(jù)挖掘和特性信息提取技術體系,通過上述技術實現(xiàn)了對風電海量數(shù)據(jù)的特性信息提取和深度挖掘。本文以冀北電網(wǎng)風力發(fā)電的海量監(jiān)測數(shù)據(jù)為依據(jù),采用多時空尺度數(shù)據(jù)挖掘和特性信息提取技術,對各個風電場出力數(shù)據(jù)進行了詳細的整理和統(tǒng)計,提出了多個風電出力特性分析評價指標,并按照風電出力的隨機性、波動性、相關性3種特性將評價指標進行歸類,從而形成了用于評價風電出力特性規(guī)律的多時空尺度指標體系,大大提高了現(xiàn)有評價指標的精確性和完整性。時間尺度上包括了分鐘級、小時級、日級等不同時間尺度,空間上從單個風場、風電場群延伸到整個冀北風電基地。風電出力隨機特性分析部分統(tǒng)計了風速和風電出力的概率分布并建立了相應的概率密度分布函數(shù)。統(tǒng)計了風速和風電出力的預測誤差分布,分析了現(xiàn)有風電出力預測誤差分布模型的缺點和不足并利用非參數(shù)估計法進行了改進,建立了風電出力預測誤差的分區(qū)分布函數(shù),進而結合風電出力點預測值得到了風電出力預測波動區(qū)間。風電出力波動特性分析部分從不同的時間尺度出發(fā)統(tǒng)計分析了風電出力單步變化率、風電高風險爬坡事件、風電出力極值、風電峰谷差貢獻率以及風電中長期出力典型日模式,可以為大規(guī)模風電并網(wǎng)后的電力系統(tǒng)調(diào)頻、調(diào)峰和風險評估分析等多個領域提供參考。風電出力相關特性分析部分分為單個風電場出力自相關和多個風電場出力互相關兩類展開分析。利用自相關系數(shù)和風電出力區(qū)間轉換概率統(tǒng)計分析了單個風電場出力的自相關特性。利用互相關系數(shù)分析了位于空間不同區(qū)域的多個風電場出力時間序列間以及風電場與風電基地出力之間的互相關特性。利用風電場之間的出力變化率,出力標準差和出力同時率分析了風電場的空間集群效應。
[Abstract]:Wind power has become the most rapid development, the most mature technology of the new clean energy, our country attaches great importance to the development of wind power, wind power installed capacity has reached the first in the world.However, due to the strong randomness and volatility of wind power generation, the large-scale grid connection of wind power has a great influence on the frequency modulation, peak-shaving, dispatching and other aspects of power system dispatching, which increases the difficulty of power grid dispatching.Therefore, only by analyzing wind power from multiple time and space scales, and fully master the characteristics of wind power, can we provide reference and key data support for the analysis and study of all aspects of power system after wind power grid connection.Better wind power large-scale grid absorption.In this paper, the shortcomings and shortcomings of the existing data mining and characteristic information extraction techniques in dealing with massive wind power output data extraction are supplemented and improved. Data reduction, data clustering and data statistics are mainly studied in this paper.Correlation analysis of four aspects of data mining and feature information extraction technology, so as to establish a more complete application in wind power performance analysis of mass data mining and feature information extraction technology system,The characteristic information extraction and depth mining of wind power magnanimity data are realized by the above technology.Based on the massive monitoring data of wind power generation in the north Hebei power grid, the data of each wind farm are sorted out and counted in detail by using the techniques of multi-space-time scale data mining and characteristic information extraction.Several evaluation indexes of wind power output characteristics are put forward, and the evaluation indexes are classified according to the randomness, fluctuation and relativity of wind power output.Therefore, a multi-space-time scale index system for evaluating the characteristics of wind power output is formed, which greatly improves the accuracy and integrity of the existing evaluation indexes.The time scale includes minute scale, hour scale, day scale and so on. In space, the wind farm group extends from a single wind field to the whole wind power base in the north of Hebei Province.The probability distribution of wind speed and output force is analyzed and the probability density distribution function is established.The prediction error distribution of wind speed and wind power output is analyzed, the shortcomings and shortcomings of the existing wind power output prediction error distribution model are analyzed, and the non-parametric estimation method is used to improve the prediction error distribution function of wind power output prediction error.Furthermore, the fluctuation interval of wind power output prediction is obtained by combining the predicted value of wind power output point.The characteristics of wind power output fluctuation are analyzed in terms of different time scales, such as wind power output single step change rate, wind power high risk climbing event, wind power output extreme value, wind power peak and valley difference contribution rate and typical day model of wind power output.It can be used as a reference for power system frequency modulation, peak-shaving and risk assessment analysis after large-scale wind power grid connection.The analysis of wind power output correlation is divided into two types: single wind farm output autocorrelation and multiple wind farm output correlation analysis.The autocorrelation characteristics of a single wind farm are analyzed by means of autocorrelation coefficient and transition probability of wind power output interval.The cross-correlation characteristics of multiple wind farm output time series located in different regions of space and between wind farm and wind power base are analyzed by using the correlation number.The spatial cluster effect of wind farm is analyzed by using the variation rate of output force, the standard deviation of output force and the simultaneous rate of output force between wind farms.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM614

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本文編號：1751614