CAJ | 학술논문

建立风电功率预测系统并提高其预测精度是大规模开发风电的关键技术之一。基于数值天气预报,建立了反向传播（BP）神经网络风电功率预测模型,并采用某风电场实际数据分析了影响该模型预测精度的因素。针对原始风速及功率序列日特性不明显、BP神经网络不能完全映射其特性的缺陷,提出了一种基于小波—BP神经网络的预测模型。该模型利用小波将风速与功率序列在不同尺度上进行分解,并使用多个BP神经网络对各频率分量进行预测,最后重构得到完整的预测结果。研究表明该模型可有效提高预测精度。
건립풍전공솔예측계통병제고기예측정도시대규모개발풍전적관건기술지일。기우수치천기예보,건립료반향전파（BP）신경망락풍전공솔예측모형,병채용모풍전장실제수거분석료영향해모형예측정도적인소。침대원시풍속급공솔서렬일특성불명현、BP신경망락불능완전영사기특성적결함,제출료일충기우소파—BP신경망락적예측모형。해모형이용소파장풍속여공솔서렬재불동척도상진행분해,병사용다개BP신경망락대각빈솔분량진행예측,최후중구득도완정적예측결과。연구표명해모형가유효제고예측정도。
Establishing the wind power prediction system and improving the prediction accuracy is one of the key techniques for exploiting wind power.Based on numerical weather prediction,a wind power prediction model using the back propagation（BP） neural network is proposed.Factors that affect the prediction accuracy are analyzed using actual data of a certain wind farm.In the light of inconspicuous day characteristic of the original wind speed and the failure of the BP neural network to completely map its power sequence,a prediction model based on wavelet-BP neural network is proposed.With the wavelet-BP neural network model,the wind speed and power sequence are decomposed into different scales.Then the sub-sequences of different frequency components are predicted using multiple BP neural networks.Finally,the output data of BP neural networks are reconstructed to obtain the complete wind power predicting results.It is shown by the research results that the prediction accuracy of wavelet-BP neural network is effectively improved.