發(fā)布時(shí)間：2018-04-15 00:06

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

【摘要】：風(fēng)電已經(jīng)成為發(fā)展最迅速、技術(shù)最為成熟的新型清潔能源,我國非常重視風(fēng)電的發(fā)展,目前風(fēng)電裝機(jī)容量已經(jīng)達(dá)到了世界第一位。但是由于風(fēng)電出力具有很強(qiáng)的隨機(jī)性和波動性,風(fēng)電的大規(guī)模并網(wǎng)對電力系統(tǒng)調(diào)度運(yùn)行的調(diào)頻、調(diào)峰、調(diào)度等多個(gè)方面產(chǎn)生了很大影響,增加了電網(wǎng)調(diào)度運(yùn)行的難度。因此,只有從多個(gè)時(shí)間和空間尺度對風(fēng)電出力進(jìn)行分析,全面掌握風(fēng)電的出力特性,才能為風(fēng)電并網(wǎng)后電力系統(tǒng)各個(gè)方面的分析研究提供參考和關(guān)鍵數(shù)據(jù)支撐,更好地進(jìn)行風(fēng)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(shù)在處理海量風(fēng)電出力數(shù)據(jù)提取風(fēng)電出力特性時(shí)的缺點(diǎn)和不足進(jìn)行了補(bǔ)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(jì)、相關(guān)性分析四個(gè)方面的數(shù)據(jù)挖掘和特性信息提取技術(shù),從而建立了較為完整的應(yīng)用在風(fēng)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(shù)體系,通過上述技術(shù)實(shí)現(xiàn)了對風(fēng)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(fēng)力發(fā)電的海量監(jiān)測數(shù)據(jù)為依據(jù),采用多時(shí)空尺度數(shù)據(jù)挖掘和特性信息提取技術(shù),對各個(gè)風(fēng)電場出力數(shù)據(jù)進(jìn)行了詳細(xì)的整理和統(tǒng)計(jì),提出了多個(gè)風(fēng)電出力特性分析評價(jià)指標(biāo),并按照風(fēng)電出力的隨機(jī)性、波動性、相關(guān)性3種特性將評價(jià)指標(biāo)進(jìn)行歸類,從而形成了用于評價(jià)風(fēng)電出力特性規(guī)律的多時(shí)空尺度指標(biāo)體系,大大提高了現(xiàn)有評價(jià)指標(biāo)的精確性和完整性。時(shí)間尺度上包括了分鐘級、小時(shí)級、日級等不同時(shí)間尺度,空間上從單個(gè)風(fēng)場、風(fēng)電場群延伸到整個(gè)冀北風(fēng)電基地。風(fēng)電出力隨機(jī)特性分析部分統(tǒng)計(jì)了風(fēng)速和風(fēng)電出力的概率分布并建立了相應(yīng)的概率密度分布函數(shù)。統(tǒng)計(jì)了風(fēng)速和風(fēng)電出力的預(yù)測誤差分布,分析了現(xiàn)有風(fēng)電出力預(yù)測誤差分布模型的缺點(diǎn)和不足并利用非參數(shù)估計(jì)法進(jìn)行了改進(jìn),建立了風(fēng)電出力預(yù)測誤差的分區(qū)分布函數(shù),進(jìn)而結(jié)合風(fēng)電出力點(diǎn)預(yù)測值得到了風(fēng)電出力預(yù)測波動區(qū)間。風(fēng)電出力波動特性分析部分從不同的時(shí)間尺度出發(fā)統(tǒng)計(jì)分析了風(fēng)電出力單步變化率、風(fēng)電高風(fēng)險(xiǎn)爬坡事件、風(fēng)電出力極值、風(fēng)電峰谷差貢獻(xiàn)率以及風(fēng)電中長期出力典型日模式,可以為大規(guī)模風(fēng)電并網(wǎng)后的電力系統(tǒng)調(diào)頻、調(diào)峰和風(fēng)險(xiǎn)評估分析等多個(gè)領(lǐng)域提供參考。風(fēng)電出力相關(guān)特性分析部分分為單個(gè)風(fēng)電場出力自相關(guān)和多個(gè)風(fēng)電場出力互相關(guān)兩類展開分析。利用自相關(guān)系數(shù)和風(fēng)電出力區(qū)間轉(zhuǎn)換概率統(tǒng)計(jì)分析了單個(gè)風(fēng)電場出力的自相關(guān)特性。利用互相關(guān)系數(shù)分析了位于空間不同區(qū)域的多個(gè)風(fēng)電場出力時(shí)間序列間以及風(fēng)電場與風(fēng)電基地出力之間的互相關(guān)特性。利用風(fēng)電場之間的出力變化率,出力標(biāo)準(zhǔn)差和出力同時(shí)率分析了風(fēng)電場的空間集群效應(yīng)。
[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(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM614

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本文編號：1751614