Fault Detection, Classification, and Location by Static Switch in Microgrids Using Wavelet Transform and Taguchi-Based Artificial Neural Network

2020 ◽  
Vol 14 (2) ◽  
pp. 2725-2735 ◽  
Author(s):  
Ying-Yi Hong ◽  
Mark Tristan Angelo M. Cabatac
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Artificial Neural

Study on Fault Signal Detection for PMLSM

2011 ◽  
Vol 84-85 ◽  
pp. 442-446
Author(s):  
Bao Yu Xu ◽  
Xiao Zhuo Xu ◽  
Yi Lun Liu ◽  
Xu Dong Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Frequency Band ◽  
Relative Energy ◽  
Normalization Method ◽  
Current Signal ◽  
Novel Method ◽  
Artificial Neural

Based on wavelet transform and artificial neural network, a novel method which takes advantage of both the multi-resolution decomposition of wavelet transform and the classification characteristics of artificial neural network is proposed for fault detection of permanent magnet linear synchronous motor (PMLSM). According to the characteristic of unhealthy PMLSM, the wavelet transform is carried out to decompose and reconstruct winding current signal. Then the energy of each frequency band as faulty features can be detected through spectrum analysis of wavelet coefficients about each frequency band. With normalization method, the feature vectors are constructed from relative energy for energy of each frequency band. The proposed method is applied to the fault detection of PMLSM, and the result of simulation proved that the wavelet neural network can effectively detect different conditions of PMLSM.

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