【摘要】：風(fēng)能即是清潔能源又是可再生能源,且取之不盡用之不竭,大力開發(fā)風(fēng)力發(fā)電產(chǎn)業(yè),將成為未來電力戰(zhàn)略部署工作的重點。在實際應(yīng)用中,由于風(fēng)的不確定性、隨機性、間歇性等特點,給風(fēng)電競價上網(wǎng)和運行調(diào)度帶來了不便。功率預(yù)測技術(shù)的出現(xiàn),使這一問題得以解決。國內(nèi)外關(guān)于風(fēng)電功率預(yù)測方面的研究成果較多,均表明,不同的預(yù)測方法可對原始數(shù)據(jù)特征有著不同的體現(xiàn),組合在一起才能夠全面、合理的利用信息來建立具有較高預(yù)測質(zhì)量的模型。本文將基于內(nèi)蒙古赤峰地區(qū)賽罕壩風(fēng)電場的實測數(shù)據(jù)來建立短期功率組合預(yù)測模型,實現(xiàn)未來一天的風(fēng)功率預(yù)測,具體如下:(1)由于歷史數(shù)據(jù)中包含大量的統(tǒng)計特征。因此本文對歷史風(fēng)速、功率數(shù)據(jù)進(jìn)行具體分析,得到風(fēng)速序列的統(tǒng)計特性、功率與風(fēng)速的關(guān)系以及影響風(fēng)功率大小的其他因素,為后續(xù)建模時特征向量的選取奠定基礎(chǔ)。(2)支持向量機作為化繁為簡的小樣本學(xué)習(xí)方法,在面臨復(fù)雜的樣本空間時具有一定優(yōu)勢。本文運用最小二乘支持向量機方法來進(jìn)行風(fēng)速預(yù)測,在參數(shù)確定方面,采用粒子群優(yōu)化算法尋優(yōu),使得傳統(tǒng)的依據(jù)經(jīng)驗來確定模型參數(shù)的方法得以改善,并對標(biāo)準(zhǔn)粒子群算法加以改進(jìn),以避免粒子因早熟收斂而陷入局部最優(yōu)。通過對所建模型的誤差評價值指標(biāo)進(jìn)行統(tǒng)計分析,評價該模型的好壞。(3)預(yù)測方法各有所長,因此本文運用不同的功率預(yù)測方法,結(jié)合風(fēng)速預(yù)測的輸出,對未來一天的功率進(jìn)行預(yù)測。通過對模型誤差評價指標(biāo)的分析,選取較為互補的兩種方法作為功率組合預(yù)測模型的元素,即基于同一組數(shù)據(jù)的預(yù)測誤差曲線走勢相反。(4)將兩種單項預(yù)測方法進(jìn)行組合,采用熵權(quán)法確定組合模型權(quán)值,將同樣的輸入數(shù)據(jù)送入組合模型進(jìn)行功率預(yù)測,對運行結(jié)果進(jìn)行對比分析。誤差評價指標(biāo)除了平均絕對誤差和平均絕對百分比誤差以外,又加入絕對誤差指標(biāo)來進(jìn)一步約束。結(jié)果表明組合模型比任一單項預(yù)測模型的效果都要好;再進(jìn)一步縮短數(shù)據(jù)采樣時間間隔,運用組合模型重新預(yù)測,由于數(shù)據(jù)特征更加充實,模型的預(yù)測精度又得以提升。為證明模型的泛化特性,本文對多組數(shù)據(jù)進(jìn)行測試、檢驗,均得出較好效果,表明該預(yù)測模型適合當(dāng)?shù)仫L(fēng)電場使用。
[Abstract]:Wind energy is not only clean energy but also renewable energy, and it is inexhaustible. The development of wind power industry will be the focus of future power strategic deployment. In practical application, due to the characteristics of wind uncertainty, randomness and intermittence, wind power bidding has brought inconvenience to online access and operation scheduling. With the emergence of power prediction technology, this problem can be solved. There are many research results on wind power prediction at home and abroad, all of which show that different forecasting methods can reflect the characteristics of the original data differently, and only when combined, can they be fully integrated. Reasonable use of information to establish a model with high prediction quality. Based on the measured data of Saihanba wind farm in Chifeng area of Inner Mongolia, a short-term combined forecasting model of wind power is established in this paper. The results are as follows: (1) due to the large number of statistical characteristics contained in the historical data, the short-term wind power prediction model will be realized in the future. In this paper, the historical wind speed and power data are analyzed in detail, and the statistical characteristics of wind speed series, the relationship between power and wind speed, and other factors affecting wind power are obtained. (2) support vector machine (SVM), as a small sample learning method for simplifying complexity, has some advantages in the face of complex sample space. In this paper, the least square support vector machine method is used to predict the wind speed. In the aspect of parameter determination, the particle swarm optimization algorithm is used to optimize the model parameters, which improves the traditional method to determine the model parameters according to the experience. The standard particle swarm optimization algorithm is improved to prevent particles from falling into local optimization due to premature convergence. Through the statistical analysis of the error evaluation value index of the model, the quality of the model is evaluated. (3) the forecasting methods have their own strong points, so this paper uses different power forecasting methods, combined with the output of wind speed prediction, Forecast the power of the next day. Through the analysis of model error evaluation index, two complementary methods are selected as the elements of power combination prediction model, that is, the prediction error curve based on the same set of data has the opposite trend. (4) the two single prediction methods are combined. The weight of the combined model is determined by entropy method, and the same input data is put into the combined model for power prediction, and the running results are compared and analyzed. In addition to the average absolute error and the average absolute percentage error, the index of error evaluation is further constrained by adding the index of absolute error. The results show that the combined model has better effect than any single forecasting model, and further shortens the time interval of data sampling and uses the combined model to re-forecast, because the data features are more abundant, the prediction accuracy of the model can be improved again. In order to prove the generalization characteristics of the model, this paper tests and tests many sets of data, and gets a good result, which shows that the prediction model is suitable for local wind farms.
【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】：2017
【分類號】：TM614

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