Fault Detection and Classification in Microgrid Using Wavelet Transform and Artificial Neural Network

2020 ◽  
pp. 17-31
Author(s):  
Priya Singh ◽  
Nitin Singh ◽  
Niraj Kumar Choudhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Detection And Classification ◽  
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.

scholarly journals Fault detection and classification in wind turbine by using artificial neural network

2019 ◽  
Vol 10 (3) ◽  
pp. 1687
Author(s):  
N. F. Fadzail ◽  
S. Mat Zali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wind Turbine ◽  
Renewable Energy Sources ◽  
Maintenance Cost ◽  
Ann Model ◽  
Fault Detection And Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Wind turbine is one of the present renewable energy sources that has become the most popular. The operational and maintenance cost is continuously increasing, especially for wind generator. Early fault detection is very important to optimise the operational and maintenance cost. The goal of this project is to study fault detection and classification for a wind turbine (WT) by using artificial neural network (ANN). In this project, a single phase fault was placed at 9 MW doubly-fed induction generator (DFIG) WT in MATLAB Simulink. The WT was tested under different conditions, i.e., normal condition, fault at Phase A, Phase B and Phase C. The simulation results were used as inputs in the ANN model for training. Then, a new set of data was taken under different conditions as inputs for ANN fault classifier. The target outputs of ANN fault classifier were set as ‘0’ or ‘1’, based on the fault condition. Results obtained showed that the ANN fault classifier outputs had followed the target outputs. In conclusion, the WT fault detection and classification method by using ANN were successfully developed.

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