本文關(guān)鍵詞：基于混沌理論的風(fēng)電功率實(shí)時預(yù)測研究　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)電功率 超短期 實(shí)時預(yù)測 預(yù)測誤差 混沌

【摘要】：近十幾年,我國風(fēng)能的開發(fā)利用處于快速發(fā)展階段,風(fēng)電裝機(jī)容量以及風(fēng)電并網(wǎng)情況增長較快,但是因?yàn)榻仫L(fēng)具有間歇特征,風(fēng)電功率在一定程度上具有隨機(jī)特征,風(fēng)電并網(wǎng)后,風(fēng)電功率的大幅度波動變化會對電力系統(tǒng)正常運(yùn)行、合理分配調(diào)度等方面造成影響。掌握風(fēng)電功率的時空分布規(guī)律、對風(fēng)電功率進(jìn)行較為準(zhǔn)確的預(yù)測以及對預(yù)測誤差進(jìn)行分析研究,這對風(fēng)能大規(guī)模的開發(fā)利用具有重要意義。本文以東北風(fēng)電場的有功功率數(shù)據(jù)為例,分析研究風(fēng)電功率的時空分布特征。為了客觀認(rèn)識風(fēng)電功率的波動變化特征、研究風(fēng)電功率混沌特征的時空分布特點(diǎn),本文提出了衡量風(fēng)電功率混沌特征的量化指標(biāo)—滾動最大Lyapunov指數(shù),并對混沌特征的時間和空間分布特征進(jìn)行分析驗(yàn)證;利用自相關(guān)系數(shù)圖和周期圖驗(yàn)證了風(fēng)電功率序列中存在周期性分量這一特征,然后利用傅里葉變換提取周期分量,并且利用遞歸圖法驗(yàn)證了剩余分量具有混沌特征;利用集合經(jīng)驗(yàn)?zāi)B(tài)分解對風(fēng)電功率時間序列進(jìn)行降噪處理,然后分析降噪后的時間序列的長程相關(guān)性和分形特征。針對風(fēng)電功率預(yù)測的研究,本文提出了基于混沌理論的三種不同的預(yù)測方法:基于局域一階加權(quán)法的風(fēng)電功率超短期預(yù)測、校正的Lyapunov指數(shù)多步預(yù)測模型以及實(shí)時提取周期分量的組合預(yù)測模型。其中,基于局域一階加權(quán)法的風(fēng)電功率超短期預(yù)測模型是以距離作為鄰近相點(diǎn)的選擇判據(jù)構(gòu)建預(yù)測模型;校正的Lyapunov指數(shù)多步預(yù)測模型則以Lyapunov指數(shù)預(yù)測模型為基礎(chǔ),并對滾動預(yù)測時的預(yù)測值進(jìn)行校正;實(shí)時提取周期分量的組合預(yù)測把序列分解為周期分量和剩余分量,然后把兩個分量各自的預(yù)測值合并后加入到原序列中,針對新的風(fēng)電功率序列再次分解和預(yù)測。針對風(fēng)電功率預(yù)測誤差的研究,本文以混沌理論為基礎(chǔ),基于東北風(fēng)電場的有功功率數(shù)據(jù),分析了風(fēng)電功率在實(shí)時預(yù)測時的單步預(yù)測誤差的概率分布,研究了風(fēng)電功率預(yù)測誤差與預(yù)測步數(shù)的關(guān)系、預(yù)測誤差與風(fēng)電場出力情況的關(guān)系以及預(yù)測誤差與裝機(jī)容量之間的關(guān)系。針對風(fēng)電功率的多步預(yù)測,建立了基于VB語言的風(fēng)電場有功功率預(yù)測系統(tǒng)。該預(yù)測系統(tǒng)可直接從實(shí)時監(jiān)測系統(tǒng)中讀取功率信息,其建模簡單、運(yùn)算速度較快,能夠滿足在線使用的要求。該系統(tǒng)適用于超短期風(fēng)電功率預(yù)測,尤其適用于歷史數(shù)據(jù)量較少、氣象信息不足、僅有風(fēng)電功率序列的風(fēng)電場。
[Abstract]:In recent ten years, the development and utilization of wind energy in China is in a rapid development stage, wind power installed capacity and wind power grid has increased rapidly, but because of the intermittent characteristics of near-ground wind. Wind power has random characteristics to some extent. After wind power is connected to grid, the large fluctuation of wind power will affect the normal operation of power system. The reasonable allocation of dispatch and other aspects of the impact. Grasp the distribution of wind power in time and space, wind power more accurate prediction and analysis of the prediction error. This is of great significance to the large-scale development and utilization of wind energy. This paper takes the active power data of northeast wind farm as an example. The temporal and spatial distribution of wind power is analyzed and studied. In order to understand the fluctuation and variation of wind power, the temporal and spatial distribution characteristics of wind power chaos are studied. In this paper, the rolling maximum Lyapunov exponent, a quantitative index to measure the chaotic characteristics of wind power, is proposed, and the temporal and spatial distribution characteristics of the chaotic features are analyzed and verified. The existence of periodic components in wind power series is verified by autocorrelation and period diagrams, and then the periodic components are extracted by Fourier transform. The remaining components are proved to be chaotic by recursive graph method. The wind power time series is de-noised by means of set empirical mode decomposition, and the long range correlation and fractal characteristics of the time series after noise reduction are analyzed. The prediction of wind power is studied. In this paper, three different prediction methods based on chaos theory are proposed: wind power ultra-short-term prediction based on local first-order weighting method. The corrected multistep prediction model of Lyapunov exponent and the combination prediction model of extracting periodic components in real time. Based on the local first-order weighting method, the wind power ultra-short-term prediction model is constructed using distance as the selection criterion of adjacent phase points. The corrected Lyapunov exponent multistep prediction model is based on the Lyapunov exponent prediction model, and the prediction value of rolling prediction is corrected. The combined prediction of extracting periodic components in real time decomposes the sequence into periodic components and residual components, and then combines the predicted values of the two components and adds them to the original sequence. For the new wind power series decomposition and prediction. For wind power prediction error research, this paper based on chaos theory, based on the northeast wind farm active power data. The probability distribution of single-step prediction error of wind power in real time prediction is analyzed, and the relationship between wind power prediction error and the number of prediction steps is studied. The relationship between the prediction error and the wind farm output and the relationship between the prediction error and the installed capacity. The active power prediction system of wind farm based on VB language is established, which can directly read the power information from the real-time monitoring system. The system can meet the requirements of on-line use. The system is suitable for the prediction of ultra-short-term wind power, especially for wind farms with less historical data, insufficient meteorological information and only wind power series.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614

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本文編號：1417310