基于FKNN算法的风电功率短期预测  

Short- Term Wind Power Prediction Based on FKNN Algorithm

作　　者：郭晓利[1] 张玉萍[1] 曲朝阳[1] 任有学 辛鹏[2] 

机构地区：[1]东北电力大学信息工程学院,吉林吉林132012 [2]吉林省电力有限公司吉林供电公司安全质量监察部,吉林吉林132012

出　　处：《电测与仪表》2014年第15期1-7,共7页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51277023);吉林省自然科学基金(20130206085SF;20120338)

摘　　要：风电场输出功率预测精度的提高能够极大的减轻风力发电对电网的冲击,提高风电并网的安全性和可靠性。针对KNN(K-Nearest Neighbor algorithm)算法存在的不足进行改进,提出了FKNN(Fast K-Nearest Neighbor algorithm)算法并将其应用到风电短期功率预测当中。首先,FKNN算法基于相似数据原理,针对每个预测样本,只需遍历一次训练样本集,得出K值最大时的相似历史样本优先级队列。然后,通过逐渐缩减优先级队列的长度,产生其他K值对应的相似样本优先级队列。其次,从产生的优先级队列中获取多数类样本,并应用其输出功率的平均值对预测样本的输出功率进行预测。最后,通过对吉林省某风电场的大量历史数据进行预测分析,充分证明该算法的简单性和实用性。The improvement of wind farm’s output power prediction accuracy can greatly reduce the impact of wind power on the grid and improve the security and reliability of wind power integration. In this paper,the FKNN（Fast K- Nearest Neighbor algorithm）algorithm is proposed to improve the shortcomings of KNN（K - Nearest Neighbor algo-rithm）algorithm and is used for short - term wind power prediction. First,for each prediction sample,by using FKNN algorithm,which is based on the principle of similarity data,you can obtain the maximum priority queue of similar sample through traversing the set of training sample only one time. Then,gradually reduce the length of the priority queue to produce different size priority sub - queues of similar sample in which the majority class samples can be obtained and its average is used to predict the output power of prediction sample. Finally,the algorithm’s simplici-ty and practicality was fully proved through the prediction of a large amount historical data of a wind farm in Jilin Prov-ince.

关 键 词：风电功率短期预测 FKNN 算法 相似数据 K - MEANS 聚类算法 

分 类 号：TM933[电气工程—电力电子与电力传动]