基于小波包理论的负荷建模数据特征提取与分类

Load Characteristic Extraction and Clustering of Modeling Data Based on Wavelet Package

把负荷建模的实测电流数据看成随机扰动电压的响应,基于小波包的分解和重构理论,采用Wpdec小波包分解函数,用db1小波包对实测建模电流信号进行3层小波包分解,用Wprcoef函数对小波分解系数进行重构,提取和构造了负荷建模数据的能量特征向量。在特征向量归一化基础上,利用减法聚类算法对特征向量进行分类处理,获得了理想的负荷分类结果。通过对动模实验室和220kV变电站实测数据的特征提取和分类实例,论证了该方法的有效性和准确性,为处理海量建模数据提供了先进的特征提取与分类处理方法。

Load current data can be regarded as stochastic disturbance of voltage. Three decompositions and re-construction of wavelet package method are used to analyze load modeling data, and the characteristic vectors of load data are accurately constructed which are used to classify load data. Load data are classified by fuzzy subtraction clustering based on the standardized characteristic vectors. The validity and veracity of the method proposed have been proved by characteristics extraction and clustering for dynamic lab data and transformer substation data. The high precision and convergence make it significant for characteristic extraction and clustering of bulk modeling data.