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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


Fault Identification in T-Connection Transmission Lines based on Probabilistic Neural Network and Wave Impedance Angle

Journal of Vibration Testing and System Dynamics 5(1) (2021) 73--85 | DOI:10.5890/JVTSD.2021.03.005

Jie Yang$^{1}$, Hao Wu$^{1}$ , Qiao-mei Wang$^{1}$, Dong Li$^{1}$, Song-hai Fan$^{2}$

$^1$ Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China

$^2$ Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu 610000, China

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In order to improve the accuracy of internal and external fault identification algorithms for T-connected transmission lines, a new method for fault identification of T-connected transmission line based on the probabilistic neural network and the angle of wave impedance is proposed. Extract the initial voltage and current phasor information of each traveling wave protection unit at a specific frequency based on the S transform, then calculate the included angle of the measured wave impedance at this frequency, and use this to form a T-connection transmission line fault characteristic sample set. The T-connection transmission line fault feature training sample set is input into the probabilistic neural network training and testing to establish a fault recognition model to identify the fault. Simulation results show that the proposed algorithm can accurately identify the internal and external faults of the T-connection transmission line and the specific branch roads outside the zone.


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