發(fā)布時間：2018-05-15 03:32

  本文選題：風(fēng)電功率 + 短期預(yù)測��； 參考：《華北電力大學(xué)》2015年碩士論文

【摘要】：隨著世界能源短缺、國家能源供應(yīng)安全形勢的日趨嚴峻,風(fēng)能等可再生能源產(chǎn)業(yè)發(fā)展迅猛,風(fēng)電在電網(wǎng)中所占比重越來越大。然而相較于一些傳統(tǒng)的能源(如水電、火電),風(fēng)力發(fā)電具有波動性、間歇性、隨機性的特點,風(fēng)電大規(guī)模并網(wǎng)后會嚴重影響到電力系統(tǒng)的電能質(zhì)量和穩(wěn)定運行。風(fēng)電功率預(yù)測技術(shù)對風(fēng)電場生產(chǎn)安排和指導(dǎo)系統(tǒng)調(diào)度運行意義非常重大,因此,亟需對風(fēng)電場輸出功率預(yù)測技術(shù)開展深入研究,對風(fēng)功率進行較為準(zhǔn)確的預(yù)測。本文主要進行風(fēng)電場的短期風(fēng)功率的預(yù)測工作,以某200MW風(fēng)電場的現(xiàn)場測量數(shù)據(jù)和運行數(shù)據(jù)為基礎(chǔ),進行數(shù)據(jù)預(yù)處理、分析和短期風(fēng)功率預(yù)測。首先分析了風(fēng)電機組的功率輸出曲線,以及風(fēng)速、風(fēng)向等因素對風(fēng)功率的影響。而后應(yīng)用功率直接預(yù)測方法,利用采集到的風(fēng)速、風(fēng)向、溫度、氣壓等氣象歷史數(shù)據(jù)和風(fēng)機運行數(shù)量作為預(yù)測模型的輸入,對風(fēng)電場的短期功率進行了預(yù)測。分別建立了BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型、RBF神經(jīng)網(wǎng)絡(luò)預(yù)測模型和RBF-BP組合神經(jīng)網(wǎng)絡(luò)預(yù)測模型進行短期風(fēng)功率預(yù)測,并進行預(yù)測結(jié)果的比較。經(jīng)結(jié)果對比分析,證明RBF-BP組合神經(jīng)網(wǎng)絡(luò)預(yù)測模型預(yù)測精度更高,具有適應(yīng)時變特性的能力,以及很好的非線性映射能力,可以在風(fēng)電功率預(yù)測及其它相似的預(yù)測中應(yīng)用。
[Abstract]:With the shortage of energy in the world, the security situation of national energy supply is becoming more and more severe, the renewable energy industry of wind energy and other renewable energy industries is developing rapidly, and the proportion of wind power in the power grid is increasing. However, compared to some traditional energy (such as hydropower, thermal power), wind power has the characteristics of volatility, intermittence and randomness, and the wind power is strict with the grid. The power quality and the stable operation of the power system are seriously affected. The prediction technology of wind power is of great significance to the scheduling and operation of the wind farm production arrangement and guidance system. Therefore, it is urgent to carry out an in-depth study on the prediction technology of the output power of the wind farm and to make a more accurate prediction of the wind power. This paper mainly carries out the short-term wind power of the wind farm. On the basis of the field measurement data and operating data of a 200MW wind farm, data preprocessing, analysis and short-term wind power prediction are carried out. First, the power output curve of the wind turbine and the influence of wind speed, wind direction and other factors on wind power are analyzed. Then the direct prediction method of power is used to use the wind speed collected. The wind direction, temperature, air pressure and other meteorological historical data are used as the input of the prediction model, and the short-term power of the wind farm is predicted. The BP neural network prediction model, the RBF neural network prediction model and the RBF-BP combination neural network prediction model are used to predict the short-term wind power, and the prediction results are compared. Through the comparison and analysis of the results, it is proved that the prediction model of RBF-BP combined neural network prediction model is more accurate, has the ability to adapt to time-varying characteristics, and has good nonlinear mapping ability, and can be applied to wind power prediction and other similar prediction.

【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;TP183

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本文編號：1890799