本文選題：風電功率預測 + 風速修正��； 參考：《內蒙古大學》2017年碩士論文

【摘要】：隨著全球經濟的飛速發(fā)展,對支撐經濟發(fā)展的能源需求量越來越大。由于可利用的傳統(tǒng)化石能源量有限,能源供需不平衡的矛盾限制著世界各國經濟的發(fā)展,使得能源短缺成為整個人類社會面臨的問題�；剂系氖褂靡鸬沫h(huán)境污染問題越來越嚴重,逐漸成為當今人類社會共同面臨的巨大挑戰(zhàn)。風能是太陽能的一種轉化形式,是一種可再生的清潔能源,而風力發(fā)電是風能利用的主要形式。風力發(fā)電主要受風速和空氣密度的影響,風速的波動性和間歇性使風電場的輸出功率也具有波動性和間歇性,所以大規(guī)模風電并網(wǎng)會對電網(wǎng)的日常穩(wěn)定運行以及電網(wǎng)系統(tǒng)安全和電能質量均造成嚴重影響。如果能夠準確的預測出風電功率,便可以有效地改善電力系統(tǒng)運行可靠性,減少風電并網(wǎng)對電力系統(tǒng)的不利影響,并且可以更大限度的利用風力資源。本文提出基于馬爾科夫鏈的風速誤差修正方法,進行風電功率間接預測。在基于NWP風速預測值與SCADA風速值的風速誤差序列的基礎上進行馬爾科夫鏈風速誤差修正。首先對風速誤差序列進行模糊C均值狀態(tài)劃分,統(tǒng)計出初始狀態(tài)中各狀態(tài)的初始概率分布,然后建立各狀態(tài)之間的轉移概率矩陣,根據(jù)轉移概率矩陣預測出下一時刻風速誤差修正值,進而預測出風速誤差修正值,最終得到修正后的風速值。根據(jù)風速功率曲線分別對風速修正前和風速修正后的風電功率進行預測。為了對提出的馬爾科夫鏈風速誤差修正方法可行性驗證,對不同數(shù)量的風速樣本點進行馬爾科夫鏈風速誤差修正,并對修正結果的誤差進行分析,結果證實馬爾科夫鏈風速誤差修正模型可以提高風速預測精度。進行基于馬爾科夫鏈風速修正的風電功率預測,采用常用的絕對誤差、均方根誤差、平均絕對誤差、平均絕對百分比誤差四種誤差評價指標對功率預測的結果進行分析評價,基于馬爾科夫鏈風速修正的風電功率預測的誤差與風速修正前的功率預測誤差相比都有所下降,結果證實基于馬爾科夫鏈風速修正后的風電功率預測精度高于未修正風速的功率預測精度。因此本文提出的馬爾科夫鏈風速誤差修正可以提高風電功率預測精度。
[Abstract]:With the rapid development of the global economy, the energy demand for supporting economic development is increasing. Due to the limited amount of traditional fossil energy available, the contradiction between the imbalance of energy supply and demand restricts the economic development of countries in the world, which makes the energy shortage become a problem facing the whole human society. The environmental pollution caused by the use of fossil fuels is becoming more and more serious. Wind energy is a conversion form of solar energy, is a renewable clean energy, and wind power is the main form of wind energy utilization. Wind power generation is mainly affected by wind speed and air density. The fluctuation and intermittence of wind speed make the output power of wind farm also fluctuate and intermittent. Therefore, large-scale wind power grid connection will have a serious impact on the daily stable operation of the grid, as well as the security and power quality of the power system. If the wind power can be accurately predicted, it can effectively improve the reliability of the power system, reduce the adverse effects of wind power grid connection on the power system, and make greater use of wind power resources. In this paper, an error correction method of wind speed based on Markov chain is proposed to predict wind power indirectly. Based on the wind speed error series of NWP and SCADA, the Markov chain wind speed error correction is carried out. First, the fuzzy C-means state is used to divide the wind speed error series, and the initial probability distribution of each state in the initial state is calculated, and then the transition probability matrix among the states is established. According to the transfer probability matrix, the correction value of wind speed error at the next moment is predicted, and the revised wind speed value is finally obtained. According to the wind speed power curve, the wind power before and after the wind speed correction are predicted. In order to verify the feasibility of the proposed Markov chain wind speed error correction method, the Markov chain wind speed error correction is carried out on different wind speed sample points, and the error of the correction result is analyzed. The results show that the modified model of Markov chain wind speed error can improve the accuracy of wind speed prediction. Wind power prediction based on Markov chain wind speed correction is carried out. The results of power prediction are analyzed and evaluated by four kinds of error evaluation indexes, such as absolute error, root mean square error and average absolute percentage error. The error of wind power prediction based on Markov chain wind speed correction is lower than that before wind speed correction. The results show that the prediction accuracy of wind power based on modified Markov chain wind speed is higher than that of uncorrected wind speed. Therefore, the Markov chain wind speed error correction proposed in this paper can improve the prediction accuracy of wind power.
【學位授予單位】：內蒙古大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614

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