本文選題：風(fēng)速預(yù)測(cè) + 自回歸滑動(dòng)平均　； 參考：《計(jì)算機(jī)仿真》2017年10期

【摘要】：風(fēng)電場(chǎng)風(fēng)速預(yù)測(cè)是風(fēng)力發(fā)電中的重要組成部分,提高預(yù)測(cè)精度可以保障電力系統(tǒng)的可靠性。風(fēng)速受地形和天氣等因素的影響,表現(xiàn)出隨機(jī)性、波動(dòng)性和非平穩(wěn)性,導(dǎo)致傳統(tǒng)的風(fēng)速預(yù)測(cè)模型不易定階,且只能反映風(fēng)速序列的一部分信息,預(yù)測(cè)精度較低。為有效提高預(yù)測(cè)精度,提出一種新的優(yōu)化預(yù)測(cè)方法。首先采用求和模型消除原始風(fēng)速序列中的非平穩(wěn)性,其次使用擴(kuò)展自相關(guān)函數(shù)準(zhǔn)確定階,最后利用自回歸條件異方差模型進(jìn)一步提取殘差序列中的有用信息。運(yùn)用上述優(yōu)化模型對(duì)某地的風(fēng)速和風(fēng)電功率進(jìn)行預(yù)測(cè),并對(duì)比傳統(tǒng)模型的預(yù)測(cè)結(jié)果。結(jié)果表明,優(yōu)化預(yù)測(cè)模型充分提取了風(fēng)速序列中的信息,能夠更好跟蹤風(fēng)速驟變,提高了預(yù)測(cè)精度。
[Abstract]:Wind speed prediction in wind farm is an important part of wind power generation. The wind speed is affected by topography and weather, which shows randomness, volatility and non-stationarity, which leads to the difficulty of determining the order of the traditional wind speed prediction model, and it can only reflect some information of the wind speed series, and the prediction accuracy is low. In order to improve the prediction accuracy effectively, a new optimal prediction method is proposed. Firstly, the sum model is used to eliminate the nonstationarity in the original wind speed series, then the extended autocorrelation function is used to determine the order. Finally, the autoregressive conditional heteroscedasticity model is used to extract the useful information from the residual sequence. The above optimization model is used to predict the wind speed and wind power in a certain place, and the results are compared with those of the traditional model. The results show that the optimized prediction model can fully extract the information from the wind speed series, which can better track the sudden changes of the wind speed and improve the prediction accuracy.
【作者單位】： 四川大學(xué)電氣信息學(xué)院;
【基金】：四川省科技廳支撐項(xiàng)目(2016GZ0145)
【分類號(hào)】：TM614

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本文編號(hào)：2083025