CAJ | 학술논문

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 Neigh-bor 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。
풍전장수출공솔예측정도적제고능구겁대적감경풍력발전대전망적충격，제고풍전병망적안전성화가고성。침대 KNN（K - Nearest Neighbor algorithm）산법존재적불족진행개진，제출료 FKNN（Fast K - Nearest Neigh-bor algorithm）산법병장기응용도풍전단기공솔예측당중。수선，FKNN 산법기우상사수거원리，침대매개예측양본，지수편력일차훈련양본집，득출 K 치최대시적상사역사양본우선급대렬。연후，통과축점축감우선급대렬적장도，산생기타 K 치대응적상사양본우선급대렬。기차，종산생적우선급대렬중획취다수류양본，병응용기수출공솔적평균치대예측양본적수출공솔진행예측。최후，통과대길림성모풍전장적대량역사수거진행예측분석，충분증명해산법적간단성화실용성。
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.