CAJ | 학술논문

风电场功率预报是减小大规模风电并网对电网造成不良影响的有效手段,提高短期风速预测的精度是保障风电场功率预报的重要基础。提出了基于相似数据并结合小波分析的支持向量机短期风速预测方法。该方法从大量的数据样本中提取相似数据创建训练样本,采用小波分解技术将风速信号分解成低频趋势信号和高频随机信号,分别采用支持向量机理论建模,合成得到风速预测数据。仿真结果表明,相似数据有效地提高了数据的相关度,小波分解使支持向量机模型更好地拟合风速信号的低频和高频特性,提高了预测精度。通过与某风电场的实际风速数据验证,表明模型具有较强的泛化能力,程序运行时间可满足工程需要。
풍전장공솔예보시감소대규모풍전병망대전망조성불량영향적유효수단,제고단기풍속예측적정도시보장풍전장공솔예보적중요기출。제출료기우상사수거병결합소파분석적지지향량궤단기풍속예측방법。해방법종대량적수거양본중제취상사수거창건훈련양본,채용소파분해기술장풍속신호분해성저빈추세신호화고빈수궤신호,분별채용지지향량궤이론건모,합성득도풍속예측수거。방진결과표명,상사수거유효지제고료수거적상관도,소파분해사지지향량궤모형경호지의합풍속신호적저빈화고빈특성,제고료예측정도。통과여모풍전장적실제풍속수거험증,표명모형구유교강적범화능력,정서운행시간가만족공정수요。
Wind power prediction is an effective way to decrease the effects for the large-scale wind power connecting to grid.Improving accuracy of short-term wind prediction is key of wind power prediction.In this paper,a support vector machine（SVM） method was proposed based on wavelet analysis and similar data.Similar data were extracted from large amounts of data to create the training samples.The original wind speed data was decomposed into trend signal of low frequency band and random signal of high frequency band by wavelet decomposition.Different SVM speed forecasting models were trained respectively and combined to obtain forecasting results.The simulation experiments show that similar data effectively improve the relevance of data.SVM models better approximate the wind speed characteristics of low and high frequency band by wavelet decomposition and prediction accuracy is improved.By validation with the measured wind speed data of a wind farm,the model has generalization capability and good run time for meeting requirement of engineering application.