基于海上风电功率特性的预测误差对比分析  

Comparative analysis of prediction errors based on offshore wind power characteristics

作　　者：闫健 高长元[1] 于广滨 YAN JIan;GAO Changyuan;YU Guangbin(School of Economics and Management,Harbin University of Science and Technology,Harbin 150080,China;Science and Technology Office,Beijing University of Information Science and Technology,Beijing 100192,China;Yancheng Power Transmission and Intelligent Equipment Industry Research Institute,Harbin University of Science and Technology,Yancheng 224015,China)

机构地区：[1]哈尔滨理工大学经济与管理学院,黑龙江哈尔滨150080 [2]北京信息科技大学科技处,北京100192 [3]哈尔滨理工大学盐城动力传动及智能装备产业研究院,盐城江苏224015

出　　处：《计算机集成制造系统》2020年第3期648-654,共7页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(71672050,51607009),国家重点研发资助项目(2019YFB2006400)。

摘　　要：海上风电功率预测是大型风电场并网稳定运行的先决条件,风电功率预测的准确性对提高电网的质量和一致性具有重要作用。为提高风电功率预测误差,采用自回归移动平均(ARIMA)和改进的k-最近邻(kNN)两种预测方法对海上风电功率进行预测,结果表明,风电功率根据不同的特性可以用不同的预测方法进行预测,预测误差小于20%,通过改进预测方法,能够提高预测精度。最后,通过具体算例对改进的两种预测方法进行了验证,并将结果进行了对比,验证了两种算法在不同预测时间范围的有效性。Prediction of offshore wind power is a prerequisite for the stable operation of large-scale wind farms.The accuracy of wind power prediction plays an important role in improving the quality and consistency of the power grid.To improve the wind power prediction error,the Auto Regressive Integrated Moving Average(ARIMA)and the improved k-Nearest Neighbor(kNN)method were used to predict the offshore wind power.Results show that wind power could be predicted by different prediction techniques according to different characteristics,the prediction error was less than 20%,and the prediction accuracy could be improved by improving the prediction method.Finally,the improved two prediction methods were verified by a specific example,and the results were compared to verify the effectiveness of the algorithm.

关 键 词：风电功率 自回归移动平均 改进的k-最近邻 预测模型 

分 类 号：TM614[电气工程—电力系统及自动化]