Type of Document Thesis Author Quiroga, Jabid Eduardo Author's Email Address email@example.com URN etd-04282008-100002 Title Stator Winding Fault Detection for a Pmsm Using Fuzzy Logic Classifier and Neural Network Model Identification Degree Master of Science Department Mechanical Engineering, Department of Advisory Committee
Advisor Name Title David Cartes Committee Chair Chris Edrington Committee Co-Chair Jonathan Clark Committee Member Juan Ordonez Committee Member Keywords
- Fuzzy Logic
- Neural Networks
- Fault Detection
Date of Defense 2008-03-27 Availability unrestricted AbstractA negative sequence analysis coupled with fuzzy logic and neural network based approaches are applied to stator winding short circuit fault detection in a permanent magnet synchronous motors (PMSM).
A fuzzy logic based approach is implemented to generate a robust detection using the filtered negative sequence current and negative sequence impedance. The filtered negative sequence current is obtained by separating the high frequency components caused by the load fluctuation from the total negative sequence current. The filtered negative sequence current provides a quantitative evaluation on severity of the stator fault.
A MLP neural network is implemented as current predictors. The detection stage is carrying out using the negative sequence analysis of the residuals, obtained according to the difference between the actual values of currents and the current predictors. The negative sequence component of the residuals provides not only the information for detecting the fault condition but also a measurement of the level of severity of the winding short.
Validation of the methods are performed online using a PMSM experimental setup with dSPACE and Matlab/Simulink environment. The use of fuzzy logic classifier and neural network model identification improves the sensitivity of fault detection while reducing false alarm rate under load fluctuations.
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higher-speed Access Thesis_ref_dif_edrington_cartes.pdf 1.41 Mb 00:06:33 00:03:22 00:02:56 00:01:28 00:00:07