A machine learning based fault location method for power distribution systems using wavelet scattering networks

This paper proposes a novel machine learning based method for localizing single-line-to-ground faults in modern power distribution systems using single-end measurements. The challenge of identifying the faulty lateral is formulated as a support vector machine model-based classification problem, where a class represents a different part of the distribution network. The challenge of finding the exact fault distance is formulated as an ensemble model-based regression problem. Both models are trained with scattering coefficients extracted from the application of a wavelet scattering network on the captured faulty phase voltage signal. The performance of the proposed fault location method is evaluated with a comprehensive simulation study, conducted for the IEEE 34-bus test distribution system. The results demonstrate the efficacy of the proposed method in terms of fault location accuracy, as well as its sufficient insensitivity against several influencing factors, such as load, DG, external system strength, and network topology variations. Comparison of the proposed method with other well-established machine learning based fault location methods for power distribution systems reveals its great performance.