本文選題：風電功率 + 波動特性��； 參考：《東北電力大學》2017年碩士論文

【摘要】：隨著全球化石能源的消耗和電力需求的增長,開發(fā)可再生能源進行電力生產(chǎn)已成為全球各國關(guān)注的焦點,風能相比于其它能源具有顯著優(yōu)勢,成為近年來發(fā)展最為迅速的新能源發(fā)電技術(shù)。然而受自然界風的影響,風電具有隨機性、間歇性和不確定性,大規(guī)模風電并網(wǎng)對電力系統(tǒng)安全穩(wěn)定運行帶來了不利影響,因此,對風電功率進行預(yù)測具有重要的現(xiàn)實意義。本文以實際風電場為研究對象,對風電功率波動特性、超短期風電功率預(yù)測方法、風電功率預(yù)測誤差和風電功率可預(yù)測性進行了相關(guān)研究。針對大規(guī)模風電場輸出功率波動的時空分布特性,提出基于混合分布模型的風電功率波動特性概率分布描述方法,分別對相同機組數(shù)量不同采樣間隔和相同采樣間隔不同機組數(shù)量的風電功率波動的時空分布進行描述,在此基礎(chǔ)上分析了風電功率波動變化率的累積概率隨采樣間隔和機組數(shù)量的變化規(guī)律,對風電功率預(yù)測具有重要意義�；谙嚓P(guān)性分析和K近鄰算法,提出一種多輸出模型的風電功率超短期預(yù)測方法,實現(xiàn)了預(yù)測精度的提高。同時,為增強預(yù)測方法的信息互補性,組合單一預(yù)測方法,建立基于自適應(yīng)神經(jīng)模糊推理系統(tǒng)的組合預(yù)測模型,實現(xiàn)對單一風電功率預(yù)測結(jié)果的優(yōu)化。以風電場的實測數(shù)據(jù)為例,進行仿真分析,驗證了兩種預(yù)測模型的有效性,并分析匯聚效應(yīng)對預(yù)測結(jié)果的影響,有利于風電功率的預(yù)測精度的提高。針對不同功率水平的預(yù)測誤差所呈現(xiàn)不同的分布特性,提出基于混合分布模型來描述風電功率預(yù)測誤差的分布特性,通過與其它分布模型的對比,驗證了有效性。由于風電功率數(shù)據(jù)存在顯著的時間相依結(jié)構(gòu),對預(yù)測功率水平不同進行劃分,以劃分區(qū)段內(nèi)的預(yù)測誤差為統(tǒng)計樣本,進行預(yù)測誤差分布的概率密度擬合,進而求解相應(yīng)的累積概率。風電功率預(yù)測誤差的概率分析可為風電功率預(yù)測精度的提高和不確定性分析提供依據(jù)。針對風電功率預(yù)測方法無法實現(xiàn)完全絕對地無差預(yù)測的客觀事實,提出了風電場風電功率時間序列的可預(yù)測性概念,并利用近似熵和可預(yù)測系數(shù)對風電功率時間序列的可預(yù)測性分別進行定量分析,在此基礎(chǔ)上分析了不同機組數(shù)量匯聚時其近似熵的變化規(guī)律,驗證了所提指標的有效性。風電功率可預(yù)測性研究可在同一平臺上客觀地評價預(yù)測方法的優(yōu)劣,還可以為不同風電場確定切實可行的預(yù)測精度考核指標提供依據(jù)。
[Abstract]:With the global consumption of fossil energy and the increase of electricity demand, the development of renewable energy for electricity production has become the focus of attention in the world. Wind energy has a significant advantage over other energy sources. In recent years, the most rapid development of new energy generation technology. However, due to the influence of natural wind, wind power has randomness, intermittency and uncertainty. Large-scale wind power grid connection brings adverse effects on the safe and stable operation of power system, so it is of great practical significance to predict wind power. In this paper, the characteristics of wind power fluctuation, the prediction method of ultra-short-term wind power, the prediction error of wind power and the predictability of wind power are studied. According to the temporal and spatial distribution characteristics of large scale wind farm output power fluctuation, a method of describing the probability distribution of wind power fluctuation characteristics based on mixed distribution model is proposed. The time-space distribution of wind power fluctuation with the same number of units with different sampling intervals and the same sampling interval with different number of units is described respectively. On this basis, the cumulative probability of wind power fluctuation rate with sampling interval and the number of units is analyzed, which is of great significance for wind power prediction. Based on correlation analysis and K-nearest neighbor algorithm, an ultra-short-term wind power prediction method based on multi-output model is proposed, and the prediction accuracy is improved. At the same time, in order to enhance the information complementarity of forecasting methods, a combined prediction model based on adaptive neural fuzzy inference system is established to optimize the prediction results of single wind power. Taking the measured data of wind farm as an example, the validity of the two prediction models is verified, and the influence of convergent effect on the prediction results is analyzed, which is beneficial to the improvement of the prediction accuracy of wind power. In view of the different distribution characteristics of prediction errors at different power levels, a hybrid distribution model is proposed to describe the distribution characteristics of wind power prediction errors. The validity of the proposed model is verified by comparison with other distribution models. Because of the significant time-dependent structure of wind power data, the predicted power level is divided into different levels, and the prediction error in the division section is taken as the statistical sample to fit the probability density of the prediction error distribution. Then the corresponding cumulative probability is solved. The probabilistic analysis of wind power prediction error can provide the basis for the improvement of wind power prediction accuracy and uncertainty analysis. In view of the objective fact that wind power prediction method can not achieve absolute absolute difference prediction, the concept of predictability of wind power time series in wind farm is put forward. The approximate entropy and the predictable coefficient are used to quantitatively analyze the predictability of wind power time series respectively. On this basis, the variation law of approximate entropy of different units is analyzed, and the validity of the proposed index is verified. The predictive study of wind power can objectively evaluate the merits and demerits of forecasting methods on the same platform, and can also provide the basis for determining feasible evaluation indexes of prediction accuracy for different wind farms.
【學位授予單位】：東北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614

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本文編號：1839369