Fault Classification in Three Phase Self-Excited Induction Generators using Deep Neural Networks

In this paper, an algorithm is proposed for the classification of faults in three phase self-excited induction generators using deep neural networks, on the basis of their voltage and current waveforms. Three phase self-exited induction generators, which are mostly used in wind power stations, are often connected to the national grid. Therefore, the transient stability analysis of this machine, prior and post symmetrical and unsymmetrical short circuit faults is one of the main concerns in power system security and operation. In this study, voltage and current waveforms of faults have been simulated in the Simulink environment, for different conditions of fault. Following this, visual time-frequency representations of the fault signals called scalograms are created, using continuous wavelet transform. Finally, a deep convolutional neural network is used for classification of the fault signals. Experimental results show that a final accuracy of 82.14% as well as real-time inference is achieved on the validation set, using the proposed scheme.