基于模糊聚类分析的风电功率预测研究  

Research on Wind Power Forecasting Based on Fuzzy Clustering Analysis

作　　者：张传辉 田建艳[1,2] 高炜[3] 王芳[1,2] 

机构地区：[1]太原理工大学信息工程学院,太原030024 [2]太原理工大学电力系统运行与控制山西省重点实验室,太原030024 [3]太原理工大学机械工程学院,太原030024

出　　处：《太原理工大学学报》2018年第1期133-139,共7页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金资助项目(51277127)

摘　　要：提高短期风电功率预测精度是风电大规模发展的迫切要求,同时也是保障风电并网运行的关键。笔者在不增加模型复杂度的前提下,提出了聚类建模方法。该方法首先采用减法聚类与模糊C均值聚类(FCM)方法相结合对训练样本进行处理,然后建立不同聚类集下对应的预测模型库,最后将预测数据与聚类后的样本数据进行匹配,选择合理的模型进行预测。采用山西某风电场实际数据进行大量仿真,并将预测结果与单一模型结果对比,结果表明,该方法可以减少大的预测误差点数,有效提高风电功率预测精度。Improving short-term wind power forecasting accuracy is an urgent requirement for the development of large-scale wind power,and it is also the key to ensuring the integrated operation of wind power.In this paper,a method to improve the forecasting accuracy based on clustering is proposed without increasing the complexity of the model.First,training samples are processed by the fuzzy C-means clustering method optimized by subtractive clustering.Then the forecasting model base corresponding to different data set is established.Finally,different forecasting data are matched to the data in the clustered data set so that the optimized model is selected for wind power forecasting.A lot of actual data of wind farm in Shanxi province are used for simulation study.The results show that it can reduce the number of large prediction error so as to effectively improve the forecasting accuracy of wind power.

关 键 词：风电功率预测 模糊C均值聚类 神经网络 训练样本处理 减法聚类 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]