基于新型多维功率趋势聚类的风电功率预测方法  

Wind Power Prediction Method Based on Novel Multi-dimensional Power Trend Clustering

作　　者：师洪涛 闫佳 丁茂生 高峰 张智峰 李艺萱 SHI Hongtao;YAN Jia;DING Maosheng;GAO Feng;ZHANG Zhifeng;LI Yixuan(School of Electrical and Information Engineering,North Minzu University,Yinchuan 750021,China;State Grid Ningxia Electric Power,Yinchuan 750001,China)

机构地区：[1]北方民族大学电气信息工程学院,银川750021 [2]国网宁夏电力有限公司,银川750001

出　　处：《高电压技术》2022年第2期430-438,共9页High Voltage Engineering

基　　金：国家自然科学基金(52067001);宁夏自然科学基金(2021AAC03227);宁夏重点研发项目(2019BEB04004)。

摘　　要：为解决传统聚类算法对风电功率序列趋势特性的挖掘与利用较少,且对不同风电场进行可调节设计不足的问题,提出了一种基于新型多维功率趋势聚类的风电功率预测方法。该方法首先提出一种多维功率趋势相似距离度量方法,其中包括风电功率序列波动程度、波动时间及数值维度共3个维度的度量,对风电数据中的趋势特性进行挖掘;然后,采用提出的严格系数对各个维度的参与度进行调整,以适应不同风电场数据,获得较好的聚类效果;最后,将提出的多维功率趋势距离度量与传统的模糊C均值软聚类算法及Elman神经网络群相结合,构建完整的预测模型。研究结果表明:该方法能够有效挖掘风电功率序列中的趋势特性,并提高风电功率的预测精度。In order to solve the problems that the traditional clustering algorithm is seldom used to mine and exploit the wind power sequence trend characteristics,and the performance of adjustable design for different wind farms is insufficient,a wind power prediction method based on novel multi-dimensional power trend clustering is proposed.Firstly,a novel measurement method for multi-dimensional power trend similarity distance is proposed,including wind power sequence fluctuation degree,fluctuation time,and the numerical dimension of fluctuation,which can mine the trend characteristics of wind power data.Secondly,the proposed strict coefficient is used to adjust the degree of participation in each dimension to adapt to the better clustering effect of different wind farm data.Finally,the proposed multi-dimensional power trend distance measurement is combined with the traditional fuzzy C-means soft clustering algorithm and Elman neural network group to build a complete prediction model.The results of the study show that the trend characteristics of power sequence is effectively mined and the prediction accuracy of wind power is improved by the proposed method.

关 键 词：风电功率 短期预测 趋势聚类 模糊C均值软聚类 ELMAN神经网络 分类建模 

分 类 号：TM614[电气工程—电力系统及自动化]