發(fā)布時(shí)間：2018-07-26 16:13

【摘要】：隨著我國風(fēng)電裝機(jī)容量的快速增長,風(fēng)電在電網(wǎng)中占的比重越來越大,大規(guī)模風(fēng)電并網(wǎng)對(duì)電力系統(tǒng)安全運(yùn)行帶來嚴(yán)重沖擊,有效的風(fēng)電場(chǎng)功率預(yù)測(cè)可以為電網(wǎng)穩(wěn)定運(yùn)行和調(diào)度提供參考依據(jù)。本文針對(duì)大型風(fēng)電場(chǎng)傳統(tǒng)功率預(yù)測(cè)精度不高和不穩(wěn)定性問題,提出了一種大型風(fēng)電場(chǎng)分組的智能優(yōu)化功率預(yù)測(cè)模型,具體研究內(nèi)容如下:首先,對(duì)大型風(fēng)電場(chǎng)的參數(shù)特征進(jìn)行分析和規(guī)律統(tǒng)計(jì)。研究了大型風(fēng)電場(chǎng)風(fēng)速和風(fēng)向等參數(shù)的特性,分析了風(fēng)電場(chǎng)風(fēng)速、風(fēng)向、溫度與發(fā)電功率之間的關(guān)系,統(tǒng)計(jì)了整個(gè)風(fēng)場(chǎng)的發(fā)電功率的規(guī)律特征,對(duì)大型風(fēng)電場(chǎng)的參數(shù)特性有準(zhǔn)確的定位。其次,針對(duì)大型風(fēng)電場(chǎng)采集數(shù)據(jù)的不完整和壞點(diǎn)問題,通過風(fēng)機(jī)實(shí)際功率特性曲線對(duì)數(shù)據(jù)進(jìn)行剔除并采用相關(guān)系數(shù)矩陣法對(duì)數(shù)據(jù)進(jìn)行填補(bǔ)。針對(duì)噪聲等因素導(dǎo)致采集的風(fēng)速數(shù)據(jù)產(chǎn)生毛刺和尖峰現(xiàn)象,采用了新型粒子濾波器對(duì)風(fēng)場(chǎng)風(fēng)速數(shù)據(jù)進(jìn)行濾波處理,消除風(fēng)速的毛刺并使得數(shù)據(jù)得到平滑性處理,并將處理后的數(shù)據(jù)作為預(yù)測(cè)模型的輸入數(shù)據(jù)。接著,針對(duì)大型風(fēng)電場(chǎng)中單機(jī)功率預(yù)測(cè)模型的參數(shù)選取問題,通過改進(jìn)人工魚群算法對(duì)模型參數(shù)進(jìn)行優(yōu)化選取。對(duì)魚群算法中固定的視野和步長產(chǎn)生的局限性問題,本文通過增加自適應(yīng)調(diào)節(jié)因子來自動(dòng)調(diào)節(jié)魚群在覓食和追尾行為中的視野和步長,解決了魚群尋優(yōu)速度慢及易陷入局部最小問題,通過不同測(cè)試函數(shù)實(shí)驗(yàn)驗(yàn)證改進(jìn)的算法有較好的尋優(yōu)效果,最后建立了改進(jìn)魚群優(yōu)化支持向量機(jī)的風(fēng)電場(chǎng)單機(jī)的功率預(yù)測(cè)模型并對(duì)兩個(gè)典型的風(fēng)場(chǎng)的風(fēng)機(jī)進(jìn)行功率預(yù)測(cè)研究。最后,針對(duì)大型風(fēng)電場(chǎng)多臺(tái)風(fēng)機(jī)預(yù)測(cè)不穩(wěn)定以及傳統(tǒng)預(yù)測(cè)方法精度不高等缺陷問題,本文采用了一種基于風(fēng)速分布特征采樣互相關(guān)的風(fēng)電場(chǎng)分組的功率預(yù)測(cè)策略,將此策略結(jié)合改進(jìn)魚群優(yōu)化支持向量機(jī)建立了大型風(fēng)電場(chǎng)分組的智能優(yōu)化功率預(yù)測(cè)模型,通過陸地和近海兩個(gè)典型風(fēng)場(chǎng)實(shí)例仿真驗(yàn)證預(yù)測(cè)模型的應(yīng)用效果,并結(jié)合實(shí)習(xí)項(xiàng)目設(shè)計(jì)了一套風(fēng)電場(chǎng)功率預(yù)測(cè)系統(tǒng)軟件,對(duì)提出的方法進(jìn)行了工程性驗(yàn)證。
[Abstract]:With the rapid growth of the installed capacity of wind power in China, the proportion of wind power in the power network is increasing, and large-scale wind power grid connection brings serious impact on the safe operation of power system. Effective wind farm power prediction can provide reference for power grid stable operation and dispatching. Aiming at the problem of low precision and instability of traditional power prediction in large-scale wind farms, this paper presents an intelligent optimized power prediction model for grouping large wind farms. The specific research contents are as follows: firstly, The parameter characteristics of large scale wind farm are analyzed and regular statistics are made. The characteristics of wind speed and wind direction of large scale wind farm are studied. The relationship between wind speed, wind direction, temperature and generation power is analyzed. The parameter characteristics of large scale wind farm are accurately located. Secondly, aiming at the problem of incomplete and bad points of data collected by large-scale wind farms, the actual power characteristic curve of fan is used to eliminate the data and the correlation coefficient matrix method is used to fill the data. Aiming at the phenomenon of burrs and spikes caused by noise and other factors, a new particle filter is used to filter the wind speed data in the wind field to eliminate the burr of the wind speed and to smooth the data. The processed data is taken as the input data of the prediction model. Then, aiming at the parameter selection of single-machine power prediction model in large-scale wind farm, the parameters of the model are optimized by improved artificial fish swarm algorithm. For the limitation of fixed visual field and step size in fish swarm algorithm, this paper automatically adjusts the visual field and step size of fish herd in foraging and rear-end behavior by adding adaptive adjustment factor. The problem of slow searching speed and easy to fall into local minimum is solved, and the improved algorithm is proved to be effective by different test function experiments. Finally, the power prediction model of wind farm with improved fish swarm optimization support vector machine is established, and the power prediction of two typical wind field fans is studied. Finally, in view of the instability of multi-typhoon prediction in large-scale wind farms and the low precision of traditional forecasting methods, a power prediction strategy of wind farm grouping based on wind speed distribution characteristic sampling and cross-correlation is adopted in this paper. This strategy is combined with improved fish swarm optimization support vector machine to establish the intelligent optimal power prediction model for large wind farm grouping. Two typical wind field examples on land and offshore are simulated to verify the application effect of the prediction model. A set of wind farm power prediction system software is designed, and the proposed method is verified by engineering.
【學(xué)位授予單位】：上海電機(jī)學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM614

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