湍流值对风电功率预测的影响与分析

风电功率预测是缓解弃风现象的有效手段。文中针对风电波动性,提出了一种在模糊C均值聚类算法(Fuzzy C-Means algorithm,FCM)中引入湍流值IT的风电功率预测方法。在FCM算法中引入湍流值IT对训练样本进行聚类,可以进一步增强训练样本与预测样本间的相似性,避免因训练样本减少,导致风电功率波动性影响能力增大的情况。以山西某风电场实测数据为依据,在MATLAB平台上通过支持向量机(Support Vector Machine,SVM)对FCM的聚类结果进行训练和预测,仿真结果表明,FCM-IT-SVM能有效增强风电功率的相似性,减小预测误差。

Influence and analysis of turbulence value on wind power prediction

Wind power prediction is an effective means to reduce the phenomenon of abandonment of wind power. This paper provides a means that introduce turbulence value IT in Fuzzy C-Means algorithm (FCM), which can further enhance the similarity between the predicted samples and the training samples, avoid the negative impact of wind power fluctuation due to the decrease of the training samples. This method is verified on MATLAB platform, where the prediction result is generated based on measured dates from a wind farm in Shanxi by Support Vector Machine (SVM) and FCM. From the consequence we can see that FCM-IT-SVM can be able to strengthen the similarity of wind power, and reduce the error of wind power prediction effectively.