Fault detection, location, and classification method on compressed air energy storages based inter‐connected micro‐grid clusters using traveling‐waves, current injection method, on‐line wavelet, and mathematical morphology

Abstract

A new current injection technique and traveling-waves-based fast and accurate method for fault detection, location, and classification on interconnected compressed air energy storages based micro-grid clusters presented in this paper. The compressed air energy storages based micro-grid clusters increase due to some issues such as DC sources and loads expanding, energy-saving, and the power quality developing. It is necessary to recognize the fault type and location to continue service and prevent outages expansion. In this method, a circuit kit joined to the network. Fault detection is done by analyzing the coupled kits currents and the traveling-waves of the fault current and applying them to a mathematical morphology filter, in the Fault time. Determine the fault location and type done by using a mathematical morphology filter, circuit equations, and current calculations. DC pole to ground and pole to pole faults in lines, loads, and resources are considered as MTDC microgrid disturbances. The presented method was tested in a DC interconnected micro-grid clusters connected to several energy storages and renewable resources with many faults. The results represent the validity of the proposed method. The main advantages of the proposed fault location and classification technique are higher speed and accuracy than conventional approaches. This method robustly works to variations in fault resistance, sampling frequency, and operates very fine in high impedance fault conditions.