基于Kmeans聚类的配网变压器绕组材质辨识算法

Identification Algorithm of Distribution Network Transformer Winding Material Based on Kmeans Clustering

近年来由于日益白热化的配电变压器市场竞争,不少中小型企业“以铝代铜”生产变压器,对电力系统的稳定性留下隐患。当前变压器材质无损辨识方法和技术欠缺,而谐波电阻法所需设备简便、成本低,有很高的工程实用价值,但缺乏科学严谨的算法支撑。为了准确高效地对变压器绕组材质进行辨识,在已有研究的基础上提出了一种基于Kmeans聚类的辨识算法,并对算法进行了优化。该方法对统计得到的变压器绕组谐波电阻进行聚类分析,再通过方差选择法、主成分分析(PCA)降维对算法进行优化,验证了优化算法的可行性,并利用优化算法展开现场应用。分析结果表明该算法能准确地鉴别变压器绕组材质,有广阔的工程应用前景。

In recent years,due to the increasingly fierce competition in the distribution transformer market,many small and medium-sized enterprises"replace copper with aluminum"to produce transformers,leaving a hidden danger to the stability of the power system.At present,there is a lack of methods and techniques for non-destructive identification of transformer materials.The harmonic resistance method requires simple equipment,low cost,and has high engineering practical value,but it lacks scientific and rigorous algorithm support.In order to identify the material of transformer windings accurately and efficiently,we proposed an identification algorithm based on the Kmeans clustering and optimized on the basis of existing research.This method performs cluster analysis on the harmonic resistance of transformer winding obtained by statistics,then the algorithm is optimized through variance selection method and PCA dimension reduction.The optimization algorithm is verified to be feasible.The application analysis results show that the algorithm can effectively identify the material of transformer windings and has broad engineering application prospects.