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基于CFD流場預(yù)計算的復(fù)雜地形風(fēng)電場功率預(yù)測方法研究

發(fā)布時間:2019-03-27 08:46
【摘要】:由于風(fēng)能具有隨機波動性,大規(guī)模風(fēng)電并網(wǎng)給電力系統(tǒng)的安全和經(jīng)濟運行帶來不利影響,而準(zhǔn)確的風(fēng)電場功率預(yù)測是緩解上述問題的有效途徑;贑FD流場預(yù)計算的風(fēng)電場功率預(yù)測方法一方面可以針對復(fù)雜地形有目的性地細(xì)化計算模型,準(zhǔn)確模擬風(fēng)電場的空氣流動特征,通過精細(xì)的流場計算提高預(yù)測精度;另一方面將流場計算放在功率預(yù)測之前進(jìn)行,大幅提高模型的計算效率。該方法無需歷史數(shù)據(jù)訓(xùn)練建模,適用于歷史數(shù)據(jù)不足、地形復(fù)雜、氣候多變的風(fēng)電場。 論文主要內(nèi)容和研究結(jié)果包括: (1)針對復(fù)雜地形風(fēng)電場建立了功率預(yù)測的CFD模型及預(yù)計算數(shù)據(jù)庫 研究了平坦地形風(fēng)電場功率預(yù)測CFD方法的預(yù)測誤差特點,通過分析風(fēng)速、風(fēng)向?qū)︻A(yù)測誤差的影響規(guī)律,提出了基于風(fēng)況離散的預(yù)計算數(shù)據(jù)庫優(yōu)化方法,局部細(xì)化CFD模型和預(yù)計算數(shù)據(jù)庫,在準(zhǔn)確模擬風(fēng)電場流場特征的同時,盡可能提高模型的計算效率。 (2)建立了基于預(yù)計算方法的風(fēng)電場功率預(yù)測模型 預(yù)計算方法是根據(jù)流場特征建立預(yù)計算數(shù)據(jù)庫,預(yù)存儲風(fēng)電場CFD模型的計算結(jié)果,在實際運行中根據(jù)實際風(fēng)況調(diào)用數(shù)據(jù)庫中的相應(yīng)功率預(yù)測結(jié)果,實現(xiàn)風(fēng)電場功率預(yù)測。以中國西北部的某復(fù)雜地形風(fēng)電場為例驗證模型精度,結(jié)果表明,預(yù)計算方法在輸出功率短時間內(nèi)由滿發(fā)降至零出力、由零出力增至滿發(fā)以及大幅波動這三種對電網(wǎng)沖擊最大的風(fēng)電場出力變化狀態(tài)下表現(xiàn)出良好的跟蹤能力。 (3)建立了三種數(shù)值天氣預(yù)報風(fēng)速修正模型 基于最小二乘、支持向量機、徑向基神經(jīng)網(wǎng)絡(luò)原理建立了三種數(shù)值天氣預(yù)報風(fēng)速的修正模型,以提高數(shù)值天氣預(yù)報和整體功率預(yù)測的精度;分別以實測、原始NWP及三種修正NWP風(fēng)速為模型輸入,研究了不同修正方法的誤差修正效果,并分析了模型輸入誤差對基于CFD流場預(yù)計算的功率預(yù)測精度的影響。
[Abstract]:Due to the random fluctuation of wind power, the large-scale wind power grid connection brings adverse effects on the security and economic operation of the power system, and accurate wind farm power prediction is an effective way to alleviate the above-mentioned problems. On the one hand, the wind farm power prediction method based on the precomputation of CFD flow field can, on the one hand, refine the calculation model for complex terrain, accurately simulate the air flow characteristics of wind farm, and improve the prediction precision by fine calculation of flow field. On the other hand, the calculation of flow field is carried out before the power prediction, which greatly improves the computational efficiency of the model. This method does not require historical data training and modeling, and is suitable for wind farms with insufficient historical data, complex topography and changeable climate. The main contents and results of this paper are as follows: (1) the CFD model of power prediction for complex terrain wind farm is established and the prediction error characteristics of the CFD method for power prediction of flat terrain wind farm are studied, and the prediction error characteristics of CFD method for power prediction of flat terrain wind farm are studied. Based on the analysis of the influence of wind speed and wind direction on the prediction error, the optimization method of precomputation database based on wind condition discretization is put forward. The CFD model and precomputation database are refined locally, and the flow field characteristics of wind farm are simulated accurately at the same time. The computational efficiency of the model is improved as much as possible. (2) the pre-calculation method of wind farm power prediction model based on precomputation method is to establish the precomputation database according to the characteristics of the flow field, and store the calculation results of the CFD model of wind storage farm. In the actual operation, the corresponding power prediction results in the database are called according to the actual wind conditions, and the wind farm power prediction is realized. A complex terrain wind farm in northwest China is taken as an example to verify the accuracy of the model. The results show that the precomputation method decreases from full output to zero output in a short time. The three wind farms, which have the biggest impact on the power grid, show good tracking ability under the condition of zero output increasing to full generation and large fluctuation. (3) based on the principle of least square, support vector machine and radial basis function neural network, three kinds of modified models of numerical weather forecast wind speed are established, which are based on the principle of least square, support vector machine and radial basis function neural network. In order to improve the accuracy of numerical weather forecast and overall power prediction; Taking the measured, original NWP and three modified NWP wind speeds as the model inputs, the error correction effects of different correction methods are studied, and the influence of the model input errors on the power prediction accuracy based on the CFD flow field prediction is analyzed.
【學(xué)位授予單位】:華北電力大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2014
【分類號】:TM614

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