Fault Detection and Classification in Power Electronic Circuits Using Wavelet Transform and Neural Network

Abstract

Problem statement: The identification of faults in any analog circuit is highly required to ensure the reliability of the circuit. Early detection of faults in a circuit can greatly assist in maintenance of the system by avoiding possibly harmful damage borne out of the fault. Approach: A novel method for establishing a fault dictionary using Wavelet transform is presented. The Circuit Under Test (CUT) is three phase single level inverter. The transform coefficients for the fault free circuit as well as for the simulated faults of CUT are found. The Wavelet transform is applied to the output of CUT and Standard Deviation (SD) of the transform coefficients are extracted. Using the transform coefficients, fault dictionary has been formed. In order to identify the type of fault, a neural network classifier has been utilized. Results: The compatibility of wavelet analysis with the various classification techniques for fault diagnosis has been illustrated in this study. The results of the study demonstrate the suitability and viability of wavelet analysis in fault diagnosis of power electronic circuits. Conclusion: The proposed approach is found to be more reliable in accurate identification and isolation of faults using fault dictionary. Moreover, the neural classifier improves the efficiency of the system as neural networks do not require prior knowledge as they are capable of learning and evolving through a number of learning algorithms.