A Hybrid Neural Classifier for Dimensionality Reduction and Data Visualization and Its Application to Fault Detection and Classification of Induction Motors

2011 ◽  
Author(s):  
Mahnoosh Nadjarpoorsiyahkaly ◽  
Chee Peng Lim
Keyword(s):  
Fault Detection ◽  
Dimensionality Reduction ◽  
Data Visualization ◽  
Induction Motors ◽  
Fault Detection And Classification ◽  
Neural Classifier

scholarly journals Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Moath Alrifaey ◽  
Wei Hong Lim ◽  
Chun Kit Ang ◽  
Elango Natarajan ◽  
Mahmud Iwan Solihin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Learning Model ◽  
Photovoltaic System ◽  
Fault Detection And Classification ◽  
Deep Learning Model

A Time-Frequency Transform Based Fault Detectionand Classificationof STATCOM Integrated Single Circuit Transmission Line

2017 ◽  
Vol 8 (4) ◽  
pp. 1804
Author(s):  
Sanjay Kumar Mishra
Keyword(s):  
Fault Detection ◽  
Transmission Line ◽  
Reverse Flow ◽  
Current Transformer ◽  
Spectral Energy ◽  
Cycle Period ◽  
Static Synchronous Compensator ◽  
Time Frequency ◽  
Fault Detection And Classification

<p align="center"> </p><p align="center"> </p><table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>This paper discusses the time-frequency transform based fault detection and classification of STATCOM (Static synchronous compensator) integrated single circuit transmission line. Here, fast-discrete S-Transform (FDST) based time-frequency transformation is proposed for evaluation of fault detection and classification including STATCOM in transmission line. The STATCOM is placed at mid-point of transmission line. The system starts processing by extracting the current signals from both end of current transformer (CT) connected in transmission line. The current signals from CT’s are fed to FDST to compute the spectral energy (SE) of phase current at both end of the line. The differential spectral energy (DSE) is evaluated by subtracting the SE obtained from sending end and SE obtained from receiving end of the line. The DSE is the key indicator for deciding the fault pattern detection and classification of transmission line. This proposed scheme is simulated using MATLAB simulink R2010a version and successfully tested under various parameter condition such as fault resistance (Rf),source impedance (SI), fault inception angle (FIA) and reverse flow of current. The proposed approach is simple, reliable and efficient as the processing speed is very fast to detect the fault within a cycle period of FDT (fault detection time).</p></td></tr></tbody></table>

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