Wavelet-based Neural Network for recognition of faults at NHABE power substation of the Vietnam power system

This paper presents a new study of power system transient fault recognition using Wavelet Multi-Resolution Analysis (MRA) technique integrated with Neural Network. The proposed method requires less number of features as compared to conventional approach for the identification. The feature extracted through the wavelet is trained by a Probabilistic Neural Network for the classification of events. After training the neural network, the weight obtained is used to classify the Power Quality (PQ) problems. These techniques are applied to recognize different faults in the supply voltage of the Southern Vietnam power system at NHABE substation. The research results prove the techniques can be used to detect and classify a wide range of power different faults occurring in power systems with a high accurate ratio. The simulation results possess significant improvement over existing methods in signal detection and classification.