Nowadays, renewable energy is increasingly used in smart grids and microgrids to reduce the use of fossil fuels and improve network efficiency. Like all power system devices, microgrids are subject to transient and steady-state faults, such as short circuits. These faults impair reliability and consumer dissatisfaction. To accurately, automatically, and economically determine the location of a fault, a robust fault location method is needed to stabilize and repair the damaged part of the network. Given the access to the data of all nodes, the fault in these networks can be located based on the data on the two terminals. Accordingly, this paper proposes a method for determining fault distance and faulty section in the island and grid-connected microgrids. The proposed method uses distributed parameters line model and calculates the location of double-phase faults in the microgrid based on voltage and current data on both sides of each section, taking renewable energies and electric vehicles into account. At first, the measurement devices receive and store the current and voltage data at the beginning and end of each section. If a fault occurs, the fault distance is determined by calculating the difference between voltages and currents on both sides of the fault. According to the sampling rate, many voltage and current samples are obtained during the fault. The proposed method calculates a fault distance for each sample. As a result, many fault distances are obtained. These calculations are done for all sections. In the next step, the distances obtained for each section are plotted on the coordinate axis, and a curve is obtained for each section. Among the curves obtained, one curve has a global minimum, which indicates the faulty section. Other curves are ascending or descending. In addition, the global minimum point indicates the calculated distance of the fault from the beginning of the section. This method is not sensitive to electric vehicle models and distributed generation sources and uses only less than half-cycle data to execute the algorithm. The performance of the method is investigated with the simulation of a 9-bus microgrid in MATLAB/SIMULINK. The effects of changes in line parameters (two scenarios), different fault locations, fault resistance (0, 25, and 50 Ω), fault inception angles (36, 90, 180, and 270 degrees), different DGs operation modes (three scenarios), and measurements error (±3%) are studied. The maximum and minimum errors of this method are obtained to be 0.97% and 0.02%, respectively. The results indicate the high accuracy of the proposed method compared to other fault location methods.
 

Accurate fault location in a transmission line system is very important for electric utilities in order to quickly diagnose the location of the fault. Using a suitable technique for accurate fault location effectively reduces the time of fault recovery and system operation cost during maintenance. As a result, the reliability and quality of electricity delivery is improved and the economic losses caused by line outages are reduced. In this paper, an accurate two-terminal traveling wave-based fault location method for transmission lines is proposed. In this method, based on the arrival time of the first and second traveling waves caused by the short circuit fault, which are measured separately at both terminals of the transmission line, fault location and fault inception time are determined. The proposed algorithm does not need to synchronize data, transmission line  parameters, and traveling wave speed, which are sources of error in traveling wave-based fault location methods.  In order to better analyse fault-induced transient signals and detects the arrival time of travelling waves, mathematicl morphology filter (MMF) is used. Several faults on a typical 400 kV, 200 km transmission line were simulated using EMTP  and MATLAB programs. The simulation results verified the proposed algorithm is able to accurately locate faults on transmission line. Also, the proposed method is independent of fault conditions shch as fault impedance, fault type, fault inception time and fault location.