|国家科技期刊平台
首页|期刊导航|电源学报|基于PCA和EEMD的柔性直流配电网故障选线算法

基于PCA和EEMD的柔性直流配电网故障选线算法OA北大核心

Fault Line Selection Algorithm for Flexible DC Distribution Network Based on PCA and EEMD

中文摘要英文摘要

柔性直流故障选线技术的发展对直流配电网有着至关重要的作用.本文针对现有柔性直流故障存在的可利用的故障信息较少等问题,提出了 一种新算法,该算法有效利用了集合经验模态分解EEMD(ensemble empirical mode decomposition)算法、主成分分析 PCA(principal component analysis)和相关系数各自的优势.首先,提取暂态电流样本信号,采用EEMD得到以正交基函数表示的数据矩阵;接着,基于PCA进行该矩阵元素特征向量到主成分的转换,将样本信号投影到主元空间实现坐标变换,从而得到对样本数据的聚类和识别结果;最后,基于相关系数进行故障线路判别.本文算法的EEMD揭露了原始历史数据的内在变化规律,PCA能够有效选择故障有效特征.大量实验表明,该新算法准确有效,与现有其他方法相比,在故障信息不明显、不同过渡电阻方面具有优势.

The development of the flexible DC fault line selection technology plays an important role for DC distri-bution network.In this paper,a novel algorithm is proposed to solve the problem that there is less available fault infor-mation about the existing flexible DC fault,which makes full use of the advantages of ensemble empirical mode decom-position(EEMD),principal component analysis(PC A)and the correlation coefficient algorithm.First,the transient cur-rent sample signal is extracted,and the data matrix represented by the orthogonal basis function is obtained by EEMD.Then,the feature vector of the matrix element is transformed into the principal component based on PCA,and the sam-ple signal is projected into the principal component space to realize coordinate transformation,so as to obtain the clus-tering and identification results of the sample data.Finally,fault line identification is performed based on the correlation coefficient.The EEMD of the proposed algorithm reveals the internal variation law of the original historical data,while PCA can effectively select the effective fault features.A large num-ber of experiments show that the novel algorithm is accurate and effective.Compared with other existing methods,it has ad-vantages in the cases of unclear fault information and different transition resistances.

胡亚辉;韦延方;王鹏;王晓卫;曾志辉

河南理工大学电气工程与自动化学院,焦作 454000国网河南省电力公司电力科学研究院,郑州 450052

动力与电气工程

柔性直流配电网集合经验模态分解主成分分析故障选线相关系数

Flexible DC distribution networkensemble em-pirical mode decomposition(EEMD)principal component analy-sis(PCA)fault line selectioncorrelation coefficient

《电源学报》 2024 (002)

305-315 / 11

国家自然科学基金资助项目(61703144,U1804143);河南省矿山电力电子装置与控制创新型科技团队项目资助(CXTD2017085);河南省科技攻关资助项目(521RC1110)This work is supported by National Natural Science Foundation of China under the grant 61703144 and U1804143;Project of Innovative Team of Mine Power Electronic Device and Control in Henan Province under the grant CXTD2017085;Key Scientific and Technological Project in Henan Province under the grant 521RC1110

10.13234/j.issn.2095-2805.2024.2.305

评论