本文選題：超短期風(fēng)速預(yù)測(cè) + 支持向量機(jī)��； 參考：《華北電力大學(xué)》2014年碩士論文

【摘要】：能源問題已經(jīng)成為一個(gè)全球性問題,風(fēng)能作為一種清潔、可再生的新能源,其發(fā)電量不斷的提高,但是風(fēng)力發(fā)電本身具有波動(dòng)性、不穩(wěn)定性的特點(diǎn)并且在并網(wǎng)之后會(huì)給電力系統(tǒng)帶來不良沖擊,電力系統(tǒng)的安全性、平穩(wěn)性和高效性受到了很大的影響。為了減小風(fēng)電并網(wǎng)對(duì)電網(wǎng)的沖擊,合理的調(diào)度風(fēng)能資源,對(duì)風(fēng)電場(chǎng)的風(fēng)能資源進(jìn)行準(zhǔn)確預(yù)測(cè)是非常有必要的。對(duì)于中長(zhǎng)期的風(fēng)速預(yù)測(cè)研究,已經(jīng)有很多學(xué)者參與進(jìn)來并進(jìn)行了廣泛的研究,其預(yù)測(cè)效果還是不錯(cuò)的；但是對(duì)于短期和超短期的風(fēng)速預(yù)測(cè)研究還沒有達(dá)到很理想的效果,主要是由于風(fēng)速的隨機(jī)性和非平穩(wěn)性的特點(diǎn)造成的。 首先本文對(duì)風(fēng)電事業(yè)的發(fā)展現(xiàn)狀和背景做了簡(jiǎn)單介紹,對(duì)于現(xiàn)有的風(fēng)速預(yù)測(cè)模型方法進(jìn)行了詳細(xì)說明。其次主要研究了基于支持向量回歸機(jī)的風(fēng)速預(yù)測(cè)模型,隨后對(duì)支持向量機(jī)理論與支持向量回歸機(jī)原理及其實(shí)現(xiàn)問題的細(xì)節(jié)進(jìn)行了介紹。根據(jù)在線測(cè)得的風(fēng)速數(shù)據(jù)進(jìn)行單個(gè)周期的預(yù)測(cè),進(jìn)而擴(kuò)展到了多個(gè)周期的預(yù)測(cè),延長(zhǎng)了風(fēng)速預(yù)測(cè)的預(yù)測(cè)時(shí)間,實(shí)現(xiàn)了對(duì)某一點(diǎn)的風(fēng)速值的預(yù)測(cè)。最后介紹了主成分分析理論和粒子群優(yōu)化算法,并將其與支持向量機(jī)有機(jī)的結(jié)合起來,通過主成分析了解各個(gè)變量所占的比重,然后進(jìn)行不同變量的輸入模型的研究,實(shí)現(xiàn)了風(fēng)速預(yù)測(cè)由點(diǎn)到面的升級(jí),即實(shí)現(xiàn)了某一個(gè)面的平均風(fēng)速值預(yù)測(cè),提高了風(fēng)速預(yù)測(cè)的精度。在論文的最后提出了一些不足和新方法的探索。
[Abstract]:The energy problem has become a global problem, wind energy as a clean, renewable new energy, its power generation is increasing, but wind power itself is volatile, The characteristics of instability will bring bad impact to the power system after the grid connection. The security, stability and efficiency of the power system are greatly affected. In order to reduce the impact of wind power grid, it is necessary to accurately predict the wind energy resource of wind farm. For the study of wind speed prediction in the medium and long term, many scholars have participated in the research and carried out extensive research, and its prediction effect is still good, but for the short-term and ultra-short-term wind speed forecasting research has not achieved a very good effect. It is mainly caused by the randomness and non-stationarity of wind speed. Firstly, this paper introduces the current situation and background of wind power industry, and gives a detailed description of the existing wind speed forecasting model. Secondly, the wind speed prediction model based on support vector regression machine is studied, and then the theory of support vector machine, the principle of support vector regression machine and its implementation are introduced in detail. Based on the wind speed data measured online, a single period is predicted, which is extended to the prediction of multiple periods. The prediction time of wind speed prediction is extended, and the wind speed value at a certain point is predicted. Finally, the principal component analysis (PCA) theory and particle swarm optimization (PSO) algorithm are introduced, which are combined with support vector machine (SVM) to find out the proportion of each variable, and then to study the input model of different variables. The wind speed prediction is upgraded from point to surface, that is, the average wind speed value of a certain surface is forecasted, and the precision of wind speed prediction is improved. At the end of the paper, some shortcomings and new methods are put forward.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM614;TP181

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王慧勤;;基于支持向量機(jī)的短期風(fēng)速預(yù)測(cè)研究[J];寶雞文理學(xué)院學(xué)報(bào)(自然科學(xué)版);2009年01期

2 張國(guó)強(qiáng);張伯明;;基于組合預(yù)測(cè)的風(fēng)電場(chǎng)風(fēng)速及風(fēng)電機(jī)功率預(yù)測(cè)[J];電力系統(tǒng)自動(dòng)化;2009年18期

3 葉磊;駱興國(guó);;支持向量機(jī)應(yīng)用概述[J];電腦知識(shí)與技術(shù);2010年34期

4 趙杰輝,葛少云,劉自發(fā);基于主成分分析的徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)在電力系統(tǒng)負(fù)荷預(yù)測(cè)中的應(yīng)用[J];電網(wǎng)技術(shù);2004年05期

5 潘迪夫;劉輝;李燕飛;;基于時(shí)間序列分析和卡爾曼濾波算法的風(fēng)電場(chǎng)風(fēng)速預(yù)測(cè)優(yōu)化模型[J];電網(wǎng)技術(shù);2008年07期

6 杜穎;盧繼平;李青;鄧穎玲;;基于最小二乘支持向量機(jī)的風(fēng)電場(chǎng)短期風(fēng)速預(yù)測(cè)[J];電網(wǎng)技術(shù);2008年15期

7 黃文杰;傅礫;肖盛;;采用改進(jìn)模糊層次分析法的風(fēng)速預(yù)測(cè)模型[J];電網(wǎng)技術(shù);2010年07期

8 張明理;楊曉亮;滕云;徐建源;林莘;;基于主成分分析與前向反饋傳播神經(jīng)網(wǎng)絡(luò)的風(fēng)電場(chǎng)輸出功率預(yù)測(cè)[J];電網(wǎng)技術(shù);2011年03期

9 吳興華;周暉;黃梅;;基于模式識(shí)別的風(fēng)電場(chǎng)風(fēng)速和發(fā)電功率預(yù)測(cè)[J];繼電器;2008年01期

10 姚全珠;蔡婕;;基于PSO的LS-SVM特征選擇與參數(shù)優(yōu)化算法[J];計(jì)算機(jī)工程與應(yīng)用;2010年01期

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