Open AccessInrush current method of transformer based on wavelet packet and neural network
Author(s) -
Wang Wei,
Yan Lin,
Jin Tao,
Liu Hong,
Hu Fan,
Wu Dongxun
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8847
Subject(s) - inrush current , computer science , wavelet packet decomposition , wavelet , transformer , artificial neural network , network packet , control theory (sociology) , electronic engineering , wavelet transform , engineering , artificial intelligence , electrical engineering , computer network , voltage , control (management)
The transformer is an important equipment of power system; its operation state is directly related to the security and stability of the power system. Aiming at the problem that the differential protection of power transformer has been plagued by inrush current, a recognition method based on wavelet packet and the neural network is proposed. The inrush current and fault current signal are decomposed and reconstructed by using wavelet packet to extract wavelet packet reconstruction coefficients and calculate the energy of each band. These feature vectors are chosen as input values for the neural network. It has been shown by experiments that the inrush current and internal fault current can be accurately identified and the identification method can meet the requirement of the transformer inrush current real‐time identification system.