Solar microgrids fast and accurate fault detection, location and classification strategy using on-line phaselet, current injection kits', traveling-waves, and mathematical morphology

Abstract

In this paper, a new fast and accurate method for fault detection, location and classification on multi-terminal direct current (MTDC) distribution networks connected to solar distributed generation and loads presented. Some issues such as DC resources and loads expanding, and try to the power quality increasing have led to MTDC networks' development. It is important to recognize the fault type in order to continue service and prevent further damages. In this method, a circuit kit is connected to the network. Fault detection is performed with the measurement of the current of the connected kits and the traveling-waves of the fault current and applying it to a mathematical morphology filter, in the Fault time. Determine the type and location of faults using a mathematical morphology filter, circuit equations and current calculations. DC series and ground arc faults are considered as DC distribution network disturbances. The presented method was tested in a solar DC network connected to energy storages and solar resources with many faults. The results illustrate the validity of the proposed method. The main advantages of the proposed fault location and classification strategy are higher speed and accuracy than conventional approaches. The fault location error of the presented algorithm is less than 6.5 percent in the worst case. This method robustly operates to changing in sampling frequency [0.5-50 KHz], fault resistance [0.005-120 Ohm], and works very well in high impedance fault.