A New Methodology for Identifying Arc Fault by Sparse Representation and Neural Network

This paper proposes a sparse representation and fully connected neural network (SRFCNN) methodology for residential ac series arc fault identification. The SRFCNN method captures the features of idiographic signal using sparse coding and conducts intelligent feature learning and classification through neural network (NN). The methodology is elaborately structured by a pretreatment layer, a sparse representation layer, and a decision layer. The processing procedure includes the following main steps. A set of customized bases are first designed via dictionary learning to behave as precise feature descriptors rather than choosing a fixed dictionary. The pretreated input samples are accordingly transformed into a series of sparse coefficients and then fed into the NN for recognition. Experimental results indicate that the method works well in distinguishing the signature of the arcing state from that of normal state within a certain single load type, as well as recognizing the two states of various load types (resistive, inductive, capacitive, switching, etc.) all together. The general classification accuracy of every half-cycle signal in the 10 tested classes could reach 94.3% and be probably higher. The methodology's good generality and reliability for arc fault identification show its potential in other unsupervised signal feature recognition and fault detection.