中国电机工程学报2024，Vol.44Issue(10)：3850-3861,中插9,13.DOI:10.13334/j.0258-8013.pcsee.223434
基于盲源分离的工业谐波源负荷分类识别方法
Industrial Harmonic Source Load Classification and Identification Method Based on Blind Source Separation
摘要
Abstract
The traditional harmonic source identification method cannot solve the problem of non-intrusive identification of specific types of harmonic sources for industrial users.To solve the above problem,this paper proposes a method for identifying industrial harmonic source load subtypes based on blind source separation.This method facilitates non-intrusive identification of the specific type of harmonic source load,solely relying on the voltage and current data collected at the industrial customer's incoming line.First,starting from the load equivalent impedance model,a parallel circuit model with multiple load equivalent impedances for industrial users is established.Next,ensemble empirical mode decomposition and singular value decomposition are combined to determine the number of source impedance signals that constitute the integrated equivalent impedance signal of the monitoring points at the user's incoming line.Then,the fast independent component analysis is used to separate the harmonic source load equivalent impedance signal from the integrated equivalent impedance signal.Finally,the frequency characteristics of the separated harmonic source load equivalent impedance signal are matched with the typical harmonic source load,and then the subtype identification is realized.Both simulation and actual cases show that the proposed method can accurately identify the specific types of multiple harmonic source loads contained in industrial users,which obviously demonstrates its feasibility and practical applicability.关键词
谐波源识别/负荷等值阻抗/盲源分离/源信号数目估计Key words
harmonic source identification/load equivalent impedance/blind source separation/source number estimation分类
信息技术与安全科学引用本文复制引用
张逸,陈书畅,刘必杰,林才华..基于盲源分离的工业谐波源负荷分类识别方法[J].中国电机工程学报,2024,44(10):3850-3861,中插9,13.基金项目
国家自然科学基金项目(51777035).Project Supported by National Natural Science Foundation of China(51777035). （51777035）
