Abstract A novel fault diagnosis method is proposed in this paper in the distribution network based on wavelet transform and a Bayesian network. After the wavelet transform, decomposition, and reconstruction of various electrical basic quantities by amplitude, phase angle, and energy, the electrical fault feature quantity is combined according to various weights, and then the corresponding component switch fault characteristics are calculated by Bayesian. A simple Bayesian fusion of the electrical fault feature and component switch fault characteristics is used as the eigenvector of the Bayesian network, and then trained and predicted by Bayesian network. The experimental simulation results show that the fault diagnosis method for power distribution network based on wavelet transform and Bayesian network proposed in this paper has an obvious recognition degree according to the single fault feature. It is very accurate to identify the type and faulty components.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Intelligent Building Fault Diagnosis Based on Wavelet Transform and Bayesian Network


    Contributors:
    Fu, Jundong (author) / Huang, Luming (author) / Chen, Li (author) / Qiu, Yunxia (author)


    Publication date :

    2019-01-01


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Fault Diagnosis Method Based on Wavelet Transform

    Zhang, Wei | Springer Verlag | 2016



    Probability based vehicle fault diagnosis: Bayesian network method

    Huang, Yingping / McMurran, Ross / Dhadyalla, Gunwant et al. | Tema Archive | 2008


    Fault Diagnosis of ZD6 Turnout System Based on Wavelet Transform and GAPSO-FCM

    Li, Ziyu / Dai, Shenghua / Zheng, Ziyuan et al. | IEEE | 2021


    Fault Diagnosis of RAT Actuator Based on Bayesian Network

    Huang, Yan / Cai, Jing / Dai, Dingqiang | IEEE | 2020