ANN-based fault classification and location in MVDC shipboard power systems

Uninterrupted power supply is an important requirement for electric ship since it has to confront frequent travel and hostilities. However, the occurrence of faults in the shipboard power systems interrupts the power service continuity and leads to the severe damage on the electrical equipments. Faults need to be quickly detected and isolated in order to restore the power supply and prevent the massive cascading outage effect on the electrical equipments. Proper protective actions are needed to be implemented to ensure smooth and continuous operation of electric ship. This paper proposes an Artificial Neural Network (ANN) based method for the fault classification and location in the MVDC shipboard power systems using the transient information in the fault voltage and current waveforms. The proposed approach is applied to the cable of an equivalent MVDC system which is simulated using PSCAD. It is found to be efficient in detecting the type and location of the DC cable faults.