Phase Selection and Location Method of Generator Stator Winding Ground Fault Based on BP Neural Network

Qinwei Li and Wenchao Jia ()
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Qinwei Li: Department of Electrical Engineering, North China Electric Power University, No. 619 Yonghua Road, Baoding 071003, China
Wenchao Jia: Department of Electrical Engineering, North China Electric Power University, No. 619 Yonghua Road, Baoding 071003, China

Energies, 2023, vol. 16, issue 3, 1-15

Abstract: The phase selection and fault location methods of generator stator winding single-phase grounding fault are greatly affected by the transition resistance. A new phase selection and generator stator ground fault location approach based on the BP neural network is proposed in this research from a data-driven angle. This method uses a neural network to calculate the probability of three-phase fault occurrence to identify the fault phase and directly calculate the fault location that takes the amplitude and phase angle characteristics of zero-sequence voltage as input. The simulation results show that the stator ground fault phase selection and location algorithm based on the neural network can achieve correct phase selection and small positioning error, which has verified the effectiveness of the method.

Keywords: BP neural network; stator ground fault; stator grounding protection; fault phase selection and location (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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