Fault identification using wavelet transform and Petri networks | Zendy

Fang Liu | Zendy; Jian Hangli | Zendy; Qian Sun | Zendy; Zhi Wang | Zendy; Fan Chen | Zendy; Kai Liu | Zendy; Shanshan Yuan | Zendy; Jia Mengqing | Zendy; Jiang Li | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Fault identification using wavelet transform and Petri networks

Author(s) -

Fang Liu,

Jian Hangli,

Qian Sun,

Zhi Wang,

Fan Chen,

Kai Liu,

Shanshan Yuan,

Jia Mengqing,

Jiang Li

Publication year - 2021

Publication title -

iop conference series earth and environmental science

Language(s) - English

Resource type - Journals

eISSN - 1755-1307

pISSN - 1755-1315

DOI - 10.1088/1755-1315/645/1/012033

Subject(s) - phasor , fault (geology) , phasor measurement unit , computer science , wavelet transform , petri net , identification (biology) , transient (computer programming) , units of measurement , outlier , wavelet , electronic engineering , fuzzy logic , real time computing , algorithm , engineering , artificial intelligence , electric power system , power (physics) , physics , botany , quantum mechanics , seismology , biology , geology , operating system

This paper proposes the fault identification methods by using fuzzy Petri networks for the advanced application of the micro phasor measurement unit (μPMU) in the distribution network. Firstly, μPMUs are placed at multi buses of the radial distribution network, and the measured impedance is obtained by μPMU. Then, the transient characteristics of the three-phase fault current are extracted by the wavelet transform and Petri networks. Finally, the simulation verifies the effectiveness of the proposed method in 10kV distribution networks.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Empowering knowledge with every search