本文選題：風(fēng)電功率爬坡事件 + 預(yù)測(cè)��； 參考：《華北電力大學(xué)(北京)》2017年碩士論文

【摘要】：在含有高比例風(fēng)電的電力系統(tǒng)中,風(fēng)電功率爬坡事件(即風(fēng)電場(chǎng)輸出功率在短時(shí)間內(nèi)的大幅度波動(dòng)現(xiàn)象)對(duì)電網(wǎng)的沖擊已不容忽視。它會(huì)直接導(dǎo)致電力系統(tǒng)發(fā)用電不平衡,威脅電力系統(tǒng)的安全穩(wěn)定運(yùn)行,甚至造成嚴(yán)重的電力系統(tǒng)停電事故,給社會(huì)經(jīng)濟(jì)造成很大的損失。掌握風(fēng)電功率爬坡事件的發(fā)生規(guī)律,進(jìn)而對(duì)其及時(shí)、準(zhǔn)確的預(yù)測(cè)已成為風(fēng)電并網(wǎng)過(guò)程中亟待解決的問(wèn)題。論文從風(fēng)電功率爬坡事件的分布特征、影響因素及預(yù)測(cè)方法三個(gè)方面對(duì)其展開了探究。主要工作和成果如下:(1)研究了風(fēng)速的周期特性風(fēng)速的特性決定風(fēng)電功率的特性�；谛〔ǚ纸夥椒▽�(duì)隨機(jī)波動(dòng)的風(fēng)速時(shí)間序列進(jìn)行了拆解,從周期的角度對(duì)其展開了分析,并提出了周期強(qiáng)度評(píng)價(jià)指標(biāo)PI(Periodicity Intensity)和RPI(Relative Periodicity Intensity),對(duì)各周期分量的顯著特征進(jìn)行了定量描述,初步建立了風(fēng)速波動(dòng)性與周期性的內(nèi)在聯(lián)系。(2)分析了風(fēng)電場(chǎng)輸出功率爬坡事件的分布特征及影響因素分析了風(fēng)電場(chǎng)輸出功率爬坡事件的分布規(guī)律。結(jié)果表明:不同風(fēng)電場(chǎng)輸出功率爬坡事件的分布特點(diǎn)及主要影響因素有明顯不同。針對(duì)這一問(wèn)題,建立了一套具有普適性的風(fēng)電功率爬坡事件影響因素分析方法,用于確定不同風(fēng)電場(chǎng)的輸出功率爬坡事件的主導(dǎo)因素,為其預(yù)測(cè)提供基礎(chǔ)。(3)建立了基于正交實(shí)驗(yàn)與支持向量機(jī)的風(fēng)電功率爬坡事件預(yù)測(cè)模型基于風(fēng)電功率爬坡事件影響因素分析方法以及風(fēng)電功率爬坡事件與各氣象要素之間的聯(lián)系,建立了基于正交實(shí)驗(yàn)與支持向量機(jī)的風(fēng)電功率爬坡事件預(yù)測(cè)模型(OT-SVM)。該模型基于數(shù)值天氣預(yù)報(bào)(NWP),通過(guò)引入正交實(shí)驗(yàn)環(huán)節(jié)為各風(fēng)電場(chǎng)的預(yù)測(cè)模型選取最合適的氣象要素輸入量。經(jīng)算例驗(yàn)證:OT-SVM模型能夠有效提高預(yù)測(cè)精度,且具有普適性,能夠充分考慮不同風(fēng)電場(chǎng)輸出功率爬坡事件發(fā)生特性的差異,針對(duì)每個(gè)風(fēng)電場(chǎng)制定最適合的預(yù)測(cè)策略。(4)建立了基于小波分解與自回歸滑動(dòng)平均的風(fēng)電功率爬坡事件預(yù)測(cè)模型基于對(duì)風(fēng)電場(chǎng)歷史輸出功率的時(shí)間序列分析,建立了基于小波分解與自回歸滑動(dòng)平均的風(fēng)電功率爬坡事件預(yù)測(cè)模型(WT-ARMA)。針對(duì)預(yù)測(cè)過(guò)程中“全面性”與“準(zhǔn)確性”無(wú)法同時(shí)滿足的問(wèn)題,提出了“單支預(yù)測(cè),統(tǒng)籌決策”的預(yù)測(cè)策略。經(jīng)算例驗(yàn)證:WT-ARMA模型能夠有效解決高捕獲率與高準(zhǔn)確率不可兼得的問(wèn)題,實(shí)現(xiàn)了風(fēng)電功率爬坡事件的全面且準(zhǔn)確的預(yù)測(cè)。且該模型無(wú)需風(fēng)速、風(fēng)向等氣象要素的數(shù)值天氣預(yù)報(bào)值作為輸入,有效克服了數(shù)值天氣預(yù)報(bào)誤差對(duì)風(fēng)電功率爬坡事件預(yù)測(cè)結(jié)果的影響。
[Abstract]:In the power system with a high proportion of wind power, the impact of wind power climbing event (i.e. the large fluctuation of wind farm output power in a short period of time) on the power grid can not be ignored. It will directly lead to the imbalance of power system, threaten the safe and stable operation of power system, even cause serious power system blackout, and cause great losses to the society and economy. It is an urgent problem to grasp the occurrence law of wind power climbing event and to predict it timely and accurately in the process of wind power grid connection. In this paper, the distribution characteristics, influencing factors and prediction methods of wind and electric power climbing event are discussed. The main work and results are as follows: (1) the periodic characteristics of wind speed determine the characteristics of wind power. Based on the wavelet decomposition method, the wind speed time series of random wave is disassembled and analyzed from the point of view of period, and the index of periodic strength evaluation, Pi Periodicity Intensityand RPI / Relative Periodicity Intensityy, are put forward, and the significant characteristics of each cycle component are quantitatively described. The inherent relation between fluctuation and periodicity of wind speed is established. (2) the distribution characteristics of wind farm output power climbing events and the distribution law of wind farm output power climbing events are analyzed. The results show that the distribution characteristics and main influencing factors of the output power climbing events of different wind farms are obviously different. In order to solve this problem, a universal analysis method of wind and electric power climbing factors is established, which can be used to determine the dominant factors of wind farm output power climbing events. The prediction model of wind power climbing event based on orthogonal experiment and support vector machine is established. Based on the analysis method of factors influencing wind power climbing event and the relation between wind power climbing event and various meteorological elements, The prediction model of wind power climbing event based on orthogonal experiment and support vector machine is established. The model is based on the numerical weather forecast (NWP) and the orthogonal experiment is introduced to select the most suitable meteorological input for each wind farm prediction model. An example shows that the OT-SVM model can effectively improve the prediction accuracy and is universal, and can fully take into account the different characteristics of different wind farm output power climbing events. For each wind farm, the most suitable prediction strategy is made. (4) the wind power climbing event prediction model based on wavelet decomposition and autoregressive sliding average is established based on the time series analysis of the historical output power of the wind farm. The prediction model of wind power climbing event based on wavelet decomposition and autoregressive moving average is established. Aiming at the problem that "comprehensiveness" and "accuracy" can not be satisfied simultaneously in the process of prediction, this paper puts forward the forecasting strategy of "single prediction and overall decision making". It is verified by an example that the problem of high capture rate and high accuracy can be solved effectively by using the 1: WT-ARMA model, and the comprehensive and accurate prediction of wind power climbing event is realized. The model does not need the numerical weather forecast value of wind speed, wind direction and other meteorological elements as input, which effectively overcomes the influence of numerical weather forecast error on the forecast result of wind power climbing event.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614

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