风力发电中风电场风速优化预测仿真

发布时间：2018-06-29 18:49

  本文选题：风速预测 + 自回归滑动平均　； 参考：《计算机仿真》2017年10期

【摘要】：风电场风速预测是风力发电中的重要组成部分,提高预测精度可以保障电力系统的可靠性。风速受地形和天气等因素的影响,表现出随机性、波动性和非平稳性,导致传统的风速预测模型不易定阶,且只能反映风速序列的一部分信息,预测精度较低。为有效提高预测精度,提出一种新的优化预测方法。首先采用求和模型消除原始风速序列中的非平稳性,其次使用扩展自相关函数准确定阶,最后利用自回归条件异方差模型进一步提取残差序列中的有用信息。运用上述优化模型对某地的风速和风电功率进行预测,并对比传统模型的预测结果。结果表明,优化预测模型充分提取了风速序列中的信息,能够更好跟踪风速骤变,提高了预测精度。
[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.
【作者单位】： 四川大学电气信息学院;
【基金】：四川省科技厅支撑项目(2016GZ0145)
【分类号】：TM614

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