發(fā)布時間：2018-02-21 05:34

  本文關(guān)鍵詞： 風(fēng)速方差 物理特性 預(yù)測 誤差分析 小波分析　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：對風(fēng)速進(jìn)行預(yù)報是確保大規(guī)模風(fēng)電安全并網(wǎng)的重要技術(shù)手段。但是目前預(yù)報的時間分辨率為15min的風(fēng)速是一種平均意義下的風(fēng)速,而真實風(fēng)速由平均風(fēng)速和風(fēng)速的瞬時隨機(jī)波動兩部分構(gòu)成,風(fēng)速的瞬時隨機(jī)波動的研究還未被關(guān)注。在平均風(fēng)速預(yù)報的基礎(chǔ)上對風(fēng)速瞬時隨機(jī)部分進(jìn)行研究,可以提供實時風(fēng)速的更加詳細(xì)的信息,有助于電網(wǎng)系統(tǒng)制定更為詳盡的平抑風(fēng)電功率波動的策略。此外在風(fēng)機(jī)的選型及安全設(shè)計時,風(fēng)速的瞬時隨機(jī)波動也是一個重要的因素。因此本文將研究的視角投向風(fēng)速瞬時隨機(jī)波動,主要的研究工作如下:首先本文定義了風(fēng)速瞬時隨機(jī)波動的方差這一概念來研究風(fēng)速的瞬時隨機(jī)波動,并基于小波分解理論提出了風(fēng)速方差的計算方法。同時對風(fēng)速方差的物理特性進(jìn)行了研究。研究發(fā)現(xiàn):風(fēng)速方差與平均風(fēng)速之間存在多尺度調(diào)制效應(yīng);風(fēng)速方差的日變化曲線存在明顯的日周期現(xiàn)象。電力系統(tǒng)在平抑風(fēng)電功率波動時需要提前掌握未來一段時間內(nèi)的風(fēng)速信息,因此對風(fēng)速方差進(jìn)行預(yù)報具有重要的意義。對風(fēng)速方差進(jìn)行預(yù)報的前提是其具有可預(yù)報性。因此本文基于時間序列分析中的相關(guān)分析理論,提出了風(fēng)速方差可預(yù)報性分析的方法。分析結(jié)果表明風(fēng)速方差的可預(yù)報長度大致在1h至5h之間,可以進(jìn)行預(yù)報。同時對風(fēng)速方差與平均風(fēng)速的依賴關(guān)系進(jìn)行了研究。目前對于預(yù)報模型的最佳輸入并沒有很好的理論指導(dǎo),因此本文通過實驗選取預(yù)報模型的風(fēng)速方差最佳輸入維數(shù)。利用內(nèi)蒙古風(fēng)電場2013年風(fēng)速方差數(shù)據(jù)分別進(jìn)行提前10min、提前20min、提前30min、提前40min、提前50min以及提前60min時刻的風(fēng)速方差預(yù)報。選擇MAE及MSE作為預(yù)報結(jié)果的評價指標(biāo)。同時利用黑龍江風(fēng)電場2013年的風(fēng)速方差數(shù)據(jù)的驗證表明所建立的模型具有良好的穩(wěn)定性和可推廣性。在原始預(yù)報模型的基礎(chǔ)上,本文通過加入平均風(fēng)速的信息,改善了模型的預(yù)報性能,提高了預(yù)報結(jié)果的精度。最后,本文對風(fēng)速方差模型的預(yù)報誤差進(jìn)行了統(tǒng)計分析。隨著預(yù)報步長的增大,預(yù)報誤差也隨之增大,因此在預(yù)報的過程中,預(yù)報步長的選擇至關(guān)重要,否則會使預(yù)報結(jié)果的可信度降低。利用帶位移和尺度的T分布、正態(tài)分布以及極值分布對預(yù)報誤差的分布進(jìn)行擬合,結(jié)果表明預(yù)報誤差的分布最符合帶位移和尺度的T分布。而且不論是用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)報還是使用支持向量機(jī)進(jìn)行預(yù)報,預(yù)報誤差的分布都與帶位移和尺度的T分布最接近。
[Abstract]:The prediction of wind speed is an important technical means to ensure the safety of large-scale wind power grid, but the wind speed with 15min temporal resolution is an average wind speed. But the real wind speed is composed of two parts: average wind speed and instantaneous random fluctuation of wind speed. The research on instantaneous stochastic fluctuation of wind speed has not been paid attention to. The instantaneous stochastic part of wind speed is studied on the basis of average wind speed prediction. It can provide more detailed information on wind speed in real time, which is helpful for power grid system to develop more detailed strategy to stabilize the fluctuation of wind power. In addition, in the selection and safety design of fan, The instantaneous stochastic fluctuation of wind speed is also an important factor. The main research work is as follows: firstly, this paper defines the concept of the variance of the instantaneous stochastic fluctuation of wind speed to study the instantaneous random wave of wind speed. Based on the wavelet decomposition theory, the calculation method of wind speed variance is proposed, and the physical characteristics of wind speed variance are studied. It is found that there is a multi-scale modulation effect between wind speed variance and average wind speed. The diurnal variation curve of wind speed variance has obvious daily periodic phenomenon. In order to stabilize the fluctuation of wind power, the power system needs to master the wind speed information for a period of time in the future. Therefore, it is of great significance to predict the variance of wind speed. The premise of forecasting the variance of wind speed is its predictability. Therefore, based on the theory of correlation analysis in time series analysis, A method for analyzing the predictability of wind speed variance is proposed. The results show that the predictable length of wind speed variance is between 1 h and 5 h. At the same time, the dependence between wind speed variance and mean wind speed is studied. At present, there is no good theoretical guidance for the best input of the prediction model. In this paper, the best input dimension of wind speed variance of prediction model is selected by experiment. Wind speed in advance of 10 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes and 60 minutes in advance of Inner Mongolia wind farm on 2013, respectively. Velocity variance prediction. MAE and MSE are selected as the evaluation indexes of forecast results. Meanwhile, the verification of wind speed variance data of Heilongjiang wind farm on 2013 shows that the established model has good stability and extensibility. Based on the prediction model, In this paper, the prediction performance of the model is improved by adding the information of the mean wind speed, and the precision of the forecast result is improved. Finally, the prediction error of the wind speed variance model is analyzed statistically. The prediction error also increases, so the choice of the prediction step is very important in the forecast process, otherwise, the credibility of the forecast result will be reduced. Using the T distribution with displacement and scale, Normal distribution and extreme value distribution fit the distribution of prediction error. The results show that the distribution of prediction error is most consistent with T distribution with displacement and scale, and it can be predicted either by BP neural network or by support vector machine. The distribution of prediction error is the closest to the T distribution with displacement and scale.
【學(xué)位授予單位】：哈爾濱工業(yè)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：1521145