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  本文關(guān)鍵詞：大型風(fēng)電場分組建模方法及其在功率預(yù)測中的應(yīng)用　出處：《華北電力大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 分組建模 聚類算法 風(fēng)電場分組個數(shù) 輪廓系數(shù) 霍普金斯統(tǒng)計量 風(fēng)電場功率預(yù)測

【摘要】：風(fēng)電固有的波動性影響電力系統(tǒng)的安全、穩(wěn)定和經(jīng)濟(jì)運行,是大規(guī)模風(fēng)電并網(wǎng)運行的主要挑戰(zhàn)。風(fēng)電場功率預(yù)測是解決該問題的必要手段之一。利用場內(nèi)某代表位置的風(fēng)況來映射整個風(fēng)電場的輸出功率是當(dāng)前大多風(fēng)電場功率預(yù)測采用的方法,但對于大規(guī)模風(fēng)電場,此方法難以保證精度；若對每臺機組進(jìn)行建模預(yù)測,將導(dǎo)致預(yù)測計算時間過長,無法滿足電力系統(tǒng)對功率預(yù)測的要求。因此,研究既能提高預(yù)測精度,又保證計算效率的風(fēng)電場功率預(yù)測方法,是大型風(fēng)電場功率預(yù)測領(lǐng)域的關(guān)鍵問題之一�；诰垲愃惴ㄑ芯苛孙L(fēng)電場分組功率預(yù)測方法,主要工作包括：(1)研究了風(fēng)電場分組建模的影響因素。以實測風(fēng)速、實測功率及二者組合作為模型輸入,分析其對風(fēng)電場分組功率預(yù)測精度的影響程度。通過分析可得風(fēng)速是影響分組效果的主要因素,以風(fēng)速作為輸入變量,預(yù)測精度波動范圍較小而功率變量會使預(yù)測精度波動范圍較大。(2)提出了用于確定風(fēng)電場分組個數(shù)的指數(shù)。利用提出的輪廓系數(shù)和霍普金斯統(tǒng)計量指數(shù),分別從定性和定量的角度確定風(fēng)電場分組個數(shù),為建立風(fēng)電場分組模型奠定基礎(chǔ)。結(jié)果表明：霍普金斯統(tǒng)計量方法效果更好,定量的判定標(biāo)準(zhǔn)保證了確定風(fēng)電場分組個數(shù)的準(zhǔn)確性和高效性。(3)提出了一種可提高功率預(yù)測精度的風(fēng)電場分組模型。建立了基于K-means、FCM、SOM、GA-蟻群和譜聚類五種聚類算法的風(fēng)電場分組模型,通過識別機組的風(fēng)況特征和發(fā)電特征的相似性將大型風(fēng)電場分成不同的機組群,并利用相關(guān)性分析法選擇組內(nèi)代表機組。結(jié)果表明：五組聚類結(jié)果合理；譜聚類計算效率最高,而GA-蟻群計算時間最長。(4)建立了具有較強適用性的風(fēng)電場分組功率預(yù)測模型。利用多臺代表機組位置的風(fēng)況,預(yù)測整場輸出功率,比較各模型的預(yù)測精度,并對其適用性進(jìn)行分析。結(jié)果表明：風(fēng)電場分組模型可以有效降低大型風(fēng)電場功率預(yù)測的計算維度,顯著提高預(yù)測精度,其中,SOM和譜聚類模型較未分組預(yù)測精度分別提高1.67%和1.59%；計算效率最高的譜聚類適用于大型風(fēng)電場的分組建模,預(yù)測精度最高的SOM適用于中型風(fēng)電場,二者可為電力系統(tǒng)調(diào)度提供更準(zhǔn)確的功率預(yù)測信息。
[Abstract]:The inherent volatility of wind power affects the safety, stability and economic operation of the power system. Wind farm power prediction is one of the necessary methods to solve this problem. Using wind condition of a representative position in the field to map the output power of the whole wind farm is currently most of the wind farms. The method of power prediction. However, for large-scale wind farms, this method is difficult to ensure accuracy. If each unit is modeled and forecasted, the prediction time will be too long, which can not meet the power forecasting requirements of power system. Therefore, the research can improve the accuracy of prediction. It is one of the key problems in the field of large-scale wind farm power prediction to ensure the computational efficiency of wind farm power prediction method. Based on clustering algorithm, the wind farm grouping power prediction method is studied. The main work includes: (1) study the influence factors of wind farm grouping modeling, take the wind speed, power and combination of wind farm as the input of the model. The influence of wind speed on the accuracy of wind farm grouping power prediction is analyzed. The wind speed is the main factor affecting the grouping effect, and the wind speed is taken as the input variable. The index used to determine the number of wind farm groups is proposed. The contour coefficient and Hopkins statistic index are used to determine the number of wind farm groups. Determine the number of wind farm grouping from the qualitative and quantitative point of view, for the establishment of wind farm grouping model, the results show that the Hopkins statistical method is more effective. The quantitative criterion ensures the accuracy and efficiency of determining the number of wind farm groups. (3) A wind farm grouping model which can improve the accuracy of power prediction is proposed. Based on K-means, a wind farm grouping model is established. The wind farm grouping model based on FCM-SOMGA- ant colony and spectral clustering algorithm is proposed. The wind farm is divided into different units by identifying the wind characteristics of the units and the similarity of the generation characteristics. The representative units in the group are selected by correlation analysis. The results show that the cluster results of five groups are reasonable; Spectral clustering calculation efficiency is the highest, and GA-ant colony calculation time is the longest. (4) A wind farm grouping power prediction model with strong applicability is established. Forecast the output power of the whole field, compare the prediction accuracy of each model, and analyze its applicability. The results show that the wind farm grouping model can effectively reduce the calculation dimension of large-scale wind farm power prediction. The prediction accuracy was improved significantly, in which SOM and spectral clustering models improved the prediction accuracy by 1.67% and 1.59, respectively, compared with those of ungrouped models. Spectral clustering with the highest computational efficiency is suitable for grouping modeling of large-scale wind farms and SOM with the highest prediction accuracy is suitable for medium-sized wind farms. Both of them can provide more accurate power prediction information for power system dispatching.
【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】：2015
【分類號】：TM614

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