Lightweight Embedded Framework for Real-Time Fault Classification and Location in Radial Distribution Networks Under Distributed Generation

Abstract

This study develops a low-complexity data-driven method for real-time fault classification, and localization on a 20 kV radial feeder, while accounting for the computational and memory constraints of embedded hardware. The proposed approach is motivated by the changes introduced by distributed generation (DG), which alters fault-current behavior and reduces the reliability of conventional impedance-based protection. These limitations are mainly caused by reverse power flows and the additional infeed contribution from DG units during fault conditions. The proposed hybrid architecture processes time- and frequency-domain features, including symmetrical components and wavelet energy, through a dual-stage inference chain. The first stage uses a Random Forest classifier for fault-type identification, while the second stage uses a Multi-Layer Perceptron (MLP) distance estimator for fault localization. Evaluation on 23,778 MATLAB/Simulink fault scenarios, was performed using a grouped scenario-level holdout split to reduce overlap between training and test cases. The proposed approach reduces the localization error compared with the conventional impedance method, which produced errors exceeding 14 km under DG operation. The Random Forest classifier achieved 99.44% fault-type accuracy, and the MLP estimator reached a Mean Absolute Error (MAE) of 93.8 m, with localization accuracy of 99.51% within 1 km. Analytical resource profiling estimates less than 300 KB Flash and under 50 µs execution time, assuming a Cortex-M4F/M7-class target, pending hardware-in-the-loop validation.