基于主成分分析和谱聚类的短期风功率预测
摘要：风电场的大规模建设使得风电渗透率大大提高，为保证系统的安全稳定运行及风电消纳，需要对风电功率进行预测。为解决传统预测方法中数据维度过高的问题，提出一种基于主成分分析和谱聚类进行数据降维的预测方法。首先，基于主成分分析提取风电场各机组功率序列的主成分，实现对功率样本信息和预测对象的降维；其次，考虑风速波动特性和各机组的空间分布特征，对风速信息进行谱聚类，以实现样本数据的进一步降维；然后，基于风功率主成分信息与风速聚类结果，建立基于Elman神经网络的风电功率主成分预测模型，并通过逆变换最终得到风电场各机组功率的预测结果。利用江苏南通某海上风电场实际数据验证该方法，结果表明，预测结果的均方根误差明显降低，所提方法可以提高风电功率预测精度。
Abstract:The large-scale construction of wind farms leads to evident increase of wind power penetration. To ensure secure and stable operation of power grid and the accommodation of wind power, it is necessary to predict the wind power. To cope with the defect of too high data dimension in traditional prediction method, a prediction method, in which the data dimension reduction was performed was based on principal component analysis (abbr. PCA) and spectral clustering (abbr. SC), was proposed. Firstly, on the basis of PCA the principal component of power sequence of each generating unit in the wind farm was extracted to implement the dimension reduction of power sample information and the predicted object. Secondly, considering the fluctuation characteristic of wind speed and spatial distribution characteristics of each generating unit, the spectral clustering of wind speed information was conducted to realize further dimension reduction of sample data. Finally, based on the principal component information of wind power and the result of wind speed clustering an Elmer neural network-based wind power principal component prediction model was established, and by means of the inverse transformation the power prediction result of each generating unit in the wind farm was finally obtained. By use of actual data from a certain offshore wind farm in Nantong, Jiangsu Province the established method was verified. Verification results show that using the proposed method the predicted accuracy of wind power can be improved.
标题：基于主成分分析和谱聚类的短期风功率预测
英文标题：Short-term Wind Power Prediction Based on Principal Component Analysis and Spectral Clustering
作者：梅睿, 吕志勇, 顾文, 杨宏宇, 肖鹏,
关键词：风电功率预测, 主成分分析, 谱聚类, 数据降维, Elman神经网络,
发表日期：2023-01-16
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