Intelligent Fault Detection and Classification for an Unbalanced Network With Inverter-Based DG Units

In this article, a machine-learning-based fault detection and classification method is proposed. Two supervised learning-based protection modules are developed for the relays considered in the study—one to detect the fault and discriminate between the symmetrical or unsymmetrical nature of the fault and another to detect the faulty phase(s). A robust set of features using both relay voltage and current signals is utilized for developing the modules. The features are obtained using the multiresolution decomposition based on the empirical wavelet transform. The modules are tested on the unbalanced IEEE 13-node network integrated with inverter-based distributed generation systems capable of reactive power injection in the low-voltage ride-through mode of operation. Varying penetration levels and intermittent output of distributed generation, fault resistance, fault inception time, noise in the signals, and switching events occurring around the fault period are some of the unique operating scenarios considered to demonstrate the consistent performance of the relays. The simulations are performed in power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) and the protection modules are developed in MATLAB. The performance of the protection modules is evaluated using several metrics with respect to various classes of events occurring in the network.