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    李国锋

    • 教授     博士生导师   硕士生导师
    • 性别:男
    • 毕业院校:大连理工大学
    • 学位:博士
    • 所在单位:电气工程学院
    • 学科:电工理论与新技术
    • 办公地点:A3区32号楼静电与特种电源研究所201室
    • 联系方式:+86-411-84706489(O)
    • 电子邮箱:guofenli@dlut.edu.cn

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    Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

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    论文类型:期刊论文

    第一作者:Cai, Kewei

    通讯作者:Cao, WP (reprint author), Aston Univ, Sch Engn & Appl Sci, Birmingham B4 7ET, W Midlands, England.

    合写作者:Alalibo, Belema Prince,Cao, Wenping,Liu, Zheng,Wang, Zhiqiang,Li, Guofeng

    发表时间:2018-11-01

    发表刊物:ENERGIES

    收录刊物:SCIE

    卷号:11

    期号:11

    ISSN号:1996-1073

    关键字:deep stochastic configuration network (DSCN); harmonics analysis; power quality (PQ) disturbance; power system; variational mode decomposition (VMD)

    摘要:This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.