Learning-based Detection of Fault Type and Location in Electrical Distribution Networks

Faults are the primary cause of economic losses, equipment damage, and blackouts in distribution networks. These faults are categorized into various types and induce rapid fluctuations in voltage and current signals. In this paper, a machine learning-based fault detection method is considered. The proposed methodology effectively addresses the challenges of identifying fault types and locations in distribution power systems. By applying the Wavelet Packet Transform feature extraction method to superimposed three-phase voltage signals, the approach achieves high accuracy and robustness, even under noisy conditions and varying disturbances. The uncertainties associated with Renewable Energy Sources are considered, and the optimal locations of Monitoring Units are determined using a Voltage Stability Index-based optimization framework. Simulation results on a detailed IEEE 33-bus network validate the method's reliability, demonstrating its potential to enhance the efficiency and resilience of modern distribution networks.