本文選題：風(fēng)電功率預(yù)測 + 風(fēng)速修正��； 參考：《內(nèi)蒙古大學(xué)》2017年碩士論文

【摘要】：隨著全球經(jīng)濟(jì)的飛速發(fā)展,對支撐經(jīng)濟(jì)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(jīng)濟(jì)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(yán)重,逐漸成為當(dāng)今人類社會共同面臨的巨大挑戰(zhàn)。風(fēng)能是太陽能的一種轉(zhuǎn)化形式,是一種可再生的清潔能源,而風(fēng)力發(fā)電是風(fēng)能利用的主要形式。風(fēng)力發(fā)電主要受風(fēng)速和空氣密度的影響,風(fēng)速的波動性和間歇性使風(fēng)電場的輸出功率也具有波動性和間歇性,所以大規(guī)模風(fēng)電并網(wǎng)會對電網(wǎng)的日常穩(wěn)定運(yùn)行以及電網(wǎng)系統(tǒng)安全和電能質(zhì)量均造成嚴(yán)重影響。如果能夠準(zhǔn)確的預(yù)測出風(fēng)電功率,便可以有效地改善電力系統(tǒng)運(yùn)行可靠性,減少風(fēng)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(fēng)力資源。本文提出基于馬爾科夫鏈的風(fēng)速誤差修正方法,進(jìn)行風(fēng)電功率間接預(yù)測。在基于NWP風(fēng)速預(yù)測值與SCADA風(fēng)速值的風(fēng)速誤差序列的基礎(chǔ)上進(jìn)行馬爾科夫鏈風(fēng)速誤差修正。首先對風(fēng)速誤差序列進(jìn)行模糊C均值狀態(tài)劃分,統(tǒng)計(jì)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(zhuǎn)移概率矩陣,根據(jù)轉(zhuǎn)移概率矩陣預(yù)測出下一時刻風(fēng)速誤差修正值,進(jìn)而預(yù)測出風(fēng)速誤差修正值,最終得到修正后的風(fēng)速值。根據(jù)風(fēng)速功率曲線分別對風(fēng)速修正前和風(fēng)速修正后的風(fēng)電功率進(jìn)行預(yù)測。為了對提出的馬爾科夫鏈風(fēng)速誤差修正方法可行性驗(yàn)證,對不同數(shù)量的風(fēng)速樣本點(diǎn)進(jìn)行馬爾科夫鏈風(fēng)速誤差修正,并對修正結(jié)果的誤差進(jìn)行分析,結(jié)果證實(shí)馬爾科夫鏈風(fēng)速誤差修正模型可以提高風(fēng)速預(yù)測精度。進(jìn)行基于馬爾科夫鏈風(fēng)速修正的風(fēng)電功率預(yù)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(biāo)對功率預(yù)測的結(jié)果進(jìn)行分析評價,基于馬爾科夫鏈風(fēng)速修正的風(fēng)電功率預(yù)測的誤差與風(fēng)速修正前的功率預(yù)測誤差相比都有所下降,結(jié)果證實(shí)基于馬爾科夫鏈風(fēng)速修正后的風(fēng)電功率預(yù)測精度高于未修正風(fēng)速的功率預(yù)測精度。因此本文提出的馬爾科夫鏈風(fēng)速誤差修正可以提高風(fēng)電功率預(yù)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(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 祝錦舟;張焰;華月申;潘智俊;;基于馬爾科夫鏈的電力負(fù)荷優(yōu)化組合及應(yīng)用[J];電網(wǎng)技術(shù);2016年08期

2 陳凡;衛(wèi)志農(nóng);張小蓮;劉海濤;李軍;;計(jì)入風(fēng)速與風(fēng)電機(jī)組故障相關(guān)性的風(fēng)電場可靠性建模及其應(yīng)用[J];中國電機(jī)工程學(xué)報(bào);2016年11期

3 錢政;裴巖;曹利宵;王婧怡;荊博;;風(fēng)電功率預(yù)測方法綜述[J];高電壓技術(shù);2016年04期

4 薛禹勝;郁琛;趙俊華;Kang LI;Xueqin LIU;Qiuwei WU;Guangya YANG;;關(guān)于短期及超短期風(fēng)電功率預(yù)測的評述[J];電力系統(tǒng)自動化;2015年06期

5 錢曉東;劉維奇;;基于時間序列分析的風(fēng)電功率預(yù)測模型[J];電力學(xué)報(bào);2014年04期

6 葉林;趙永寧;;基于空間相關(guān)性的風(fēng)電功率預(yù)測研究綜述[J];電力系統(tǒng)自動化;2014年14期

7 劉純;曹陽;黃越輝;李鵬;孫勇;袁越;;基于時序仿真的風(fēng)電年度計(jì)劃制定方法[J];電力系統(tǒng)自動化;2014年11期

8 李英姿;賀琳;牛進(jìn)蒼;;基于馬爾可夫鏈的光伏并網(wǎng)發(fā)電量預(yù)測[J];太陽能學(xué)報(bào);2014年04期

9 修春波;任曉;李艷晴;劉明鳳;;基于卡爾曼濾波的風(fēng)速序列短期預(yù)測方法[J];電工技術(shù)學(xué)報(bào);2014年02期

10 丁華杰;宋永華;胡澤春;吳金城;范曉旭;;基于風(fēng)電場功率特性的日前風(fēng)電預(yù)測誤差概率分布研究[J];中國電機(jī)工程學(xué)報(bào);2013年34期

相關(guān)博士學(xué)位論文 前1條

1 高陽;風(fēng)電場風(fēng)電功率預(yù)測方法研究[D];沈陽農(nóng)業(yè)大學(xué);2011年

相關(guān)碩士學(xué)位論文 前2條

1 陸洪濤;偏最小二乘回歸數(shù)學(xué)模型及其算法研究[D];華北電力大學(xué);2014年

2 蔡禎祺;基于數(shù)值天氣預(yù)報(bào)NWP修正的BP神經(jīng)網(wǎng)絡(luò)風(fēng)電功率短期預(yù)測研究[D];浙江大學(xué);2012年

，

本文編號：2002263