Fault Location and Classification for MVDC Networks

This article proposes a two-step fault detection scheme for medium-voltage direct current (MVDC) networks based on wavelet analysis. The proposed grid transient classifier (GTC) is used to continuously monitor the energy of wavelet packet decomposition levels (WPDLs) for voltage and current signals to determine any transient disturbance, such as load change, pole-to-pole (PP) or pole-to-ground (PG) faults, pole-to-pole-to-ground (PPG), voltage sags, and switching. Subsequently, the type of disturbance is identified through an artificial neural network (ANN) classifier. Then, the proposed active grid impedance estimator (AGIE) injects a signal with two fundamental frequencies to evaluate grid impedance from the measurement point in new condition. Using the obtained data, fault type, location, and its severity are specified. Hardware-in-the-loop (HIL) results are provided for a shipboard grid case study to validate the real-time application of the proposed method and demonstrate its effectiveness.