Fault Detection and Classification of a Single Phase Inverter Using Artificial Neural Networks

The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over different epochs to perform fault detection or repeatedly trained with the training data until the error is reduced to a satisfactory level. The performance of neural networks for different test suites is examined using two evaluation metrics (classification accuracy and training error loss) from the standpoint of stability and convergence. The classification performance of the proposed neural network between normal and abnormal conditions is within the range of 93% and 100%. The simulation results show that the proposed network can detect faults quite efficiently, with the ability to differentiate between switching fault types. The results of this analysis on training error and accuracy are identified in tabular forms of Fault IDs and corresponding results based on different network designs and architecture