發(fā)布時(shí)間：2018-01-05 20:10

  本文關(guān)鍵詞：基于歷史氣象數(shù)據(jù)的風(fēng)電場(chǎng)風(fēng)速和風(fēng)功率預(yù)測(cè)研究　出處：《東北電力大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 風(fēng)力發(fā)電 預(yù)測(cè)研究 氣象數(shù)據(jù) 屬性分析 相關(guān)性 多步滾動(dòng)預(yù)測(cè) 預(yù)測(cè)精度

【摘要】：隨著化石能源的大量消耗和環(huán)保壓力的與日俱增,風(fēng)能作為可再生、清潔能源受到世界各國(guó)越來(lái)越高的重視。受地形、氣候及周圍環(huán)境等眾多因素的影響,風(fēng)力發(fā)電具有強(qiáng)烈的間歇性、波動(dòng)性和不確定性,給電力系統(tǒng)的發(fā)電規(guī)劃和經(jīng)濟(jì)調(diào)度帶來(lái)了危害,不利于傳統(tǒng)電網(wǎng)的安全、穩(wěn)定運(yùn)行,阻礙了風(fēng)電進(jìn)一步的推廣和應(yīng)用。因此,大規(guī)模的風(fēng)電并網(wǎng)運(yùn)行需要精準(zhǔn)的風(fēng)電功率預(yù)測(cè)。準(zhǔn)確的風(fēng)速是風(fēng)功率預(yù)測(cè)的關(guān)鍵因素,因此準(zhǔn)確的風(fēng)速預(yù)測(cè)也具有重要的現(xiàn)實(shí)意義。風(fēng)速時(shí)間序列易受溫度、氣壓、濕度等氣象因素的影響,且具有很高的輸入維度和較強(qiáng)的非線性,故不易精準(zhǔn)預(yù)測(cè)。極限學(xué)習(xí)機(jī)(ELM)及其優(yōu)化模型結(jié)構(gòu)簡(jiǎn)單、運(yùn)算效率高、泛化能力強(qiáng),可以根據(jù)問(wèn)題需要靈活地選擇隱含層節(jié)點(diǎn)數(shù)和激活函數(shù)類型,適用于復(fù)雜的非線性風(fēng)速預(yù)測(cè)工作。本文主要針對(duì)風(fēng)速和風(fēng)功率短期預(yù)測(cè)進(jìn)行研究,相關(guān)內(nèi)容如下:針對(duì)風(fēng)速屬性多、難以精度預(yù)測(cè)等特點(diǎn),本文采用互信息對(duì)屬性序列與風(fēng)速、功率序列之間的相關(guān)性進(jìn)行分析,并采用最大相關(guān)最小冗余(mRMR)進(jìn)行屬性選擇以降低輸入維度。之后,采用皮爾遜相關(guān)性系數(shù)(PCC)對(duì)預(yù)測(cè)輸入屬性數(shù)據(jù)進(jìn)行加權(quán)處理,以便凸顯關(guān)聯(lián)程度高屬性的重要度,提高預(yù)測(cè)精度。最后,采用ELM及其優(yōu)化模型開展預(yù)測(cè)研究。為了降低風(fēng)速序列信號(hào)的波動(dòng)性和非線性對(duì)電力系統(tǒng)的影響,進(jìn)一步提高風(fēng)速預(yù)測(cè)精度,可以采用信號(hào)分解方法對(duì)其處理以得到相對(duì)穩(wěn)定的子序列。本文采用變分模態(tài)分解(VMD)對(duì)初始時(shí)間序列進(jìn)行分解處理,得到一系列具有一定周期性、規(guī)律性且相對(duì)穩(wěn)定的子序列。針對(duì)每一個(gè)子序列,運(yùn)用偏自相關(guān)函數(shù)(PACF)篩選出關(guān)聯(lián)程度高的元素集合,確定網(wǎng)絡(luò)模型的輸入,選用泛化能力強(qiáng)的加權(quán)正則化極限學(xué)習(xí)機(jī)(WRELM)構(gòu)建多步滾動(dòng)預(yù)測(cè)模型,開展短期風(fēng)速預(yù)測(cè)。參照風(fēng)電機(jī)組的風(fēng)速-功率曲線,根據(jù)預(yù)測(cè)風(fēng)速可直接得出對(duì)應(yīng)時(shí)刻的風(fēng)功率。針對(duì)風(fēng)電功率大量的相關(guān)屬性,本文參照風(fēng)速預(yù)測(cè)方法,在互信息相關(guān)性分析的基礎(chǔ)上依據(jù)m RMR對(duì)候選屬性集合進(jìn)行屬性選擇,并采用PCC對(duì)其進(jìn)行重要度加權(quán),采用優(yōu)化ELM網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)擬合。最后,基于MATLAB 8.5(2015a)軟件平臺(tái)本文采用風(fēng)電場(chǎng)實(shí)測(cè)數(shù)據(jù)對(duì)上述內(nèi)容進(jìn)行仿真實(shí)驗(yàn),結(jié)果驗(yàn)證新方法的準(zhǔn)確性和實(shí)用性。
[Abstract]:With the grow with each passing day massive consumption of fossil fuels and environmental pressure, wind energy as a renewable and clean energy has attracted more and more attention all over the world. The terrain, climate and environment and many other factors, wind power has strong intermittency, volatility and uncertainty, which brings great harm to the economic power generation planning and scheduling system that is not conducive to the traditional power grid safety, stable operation, hindered the popularization and application of wind power further. Therefore, large-scale wind power grid connected wind power requires accurate forecasting. Accurate wind speed is the key factor for wind power prediction, so it has important practical significance to speed accurate prediction of wind speed time. The sequence is easily affected by the temperature, pressure, humidity and other meteorological factors influence, and has very high input dimension and strong nonlinearity, so it is not easy to accurately predict. The extreme learning machine (ELM) and The model has the advantages of simple structure, high operation efficiency, strong generalization ability, can choose the number of hidden layer nodes and activating function according to the need, applicable to complex nonlinear wind speed prediction. This paper focuses on wind speed and wind power forecasting, relevant content are as follows: according to the wind speed prediction accuracy is difficult to attribute. The characteristics of mutual information of attribute sequence and the correlation between wind speed, power series is analyzed, and the optimization (mRMR) for feature selection in order to reduce the input dimension. Then, by using Pearson correlation coefficient (PCC) were weighted attribute data to predict the input, in order to highlight the importance of a high degree of correlation attributes and improve the prediction accuracy. Finally, carry out the prediction research using ELM and its optimization model. In order to reduce the wind speed sequence signal and nonlinear wave of electricity The influence of the system, further improve the prediction accuracy of wind speed, can be used for the signal decomposition method to obtain relatively stable sequence. Using variational mode decomposition (VMD) to the initial time series decomposition, obtained a series of highly periodic sequence regularity and relatively stable for each. Sub sequences, using partial autocorrelation function (PACF) selected set is associated with a high degree of network elements, determine the model input, using weighted regularized extreme learning machine strong generalization ability (WRELM) to construct multi-step prediction model, carry out short-term wind speed forecast. Wind speed reference power curve of wind turbine, according to the forecast the wind speed and wind power can be directly obtained. The corresponding time for wind power is related to a large number of attributes, according to the wind speed prediction method based on correlation analysis, mutual information based on M RMR to the candidate Attribute set for attribute selection, and PCC is used for its important degree, is predicted by the fitting optimization ELM network. Finally, based on the MATLAB 8.5 (2015a) software platform used in this simulation experiment on the content of the measured data of a wind farm, the results verify the accuracy and practicability of the new method.

【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614

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