Showing 4 results for Fault Location
M. Farshad, J. Sadeh,
Volume 9, Issue 3 (9-2013)
Abstract
In this paper, an approach is proposed for accurate locating of single phase faults in transmission lines using voltage signals measured at one-end. In this method, harmonic components of the voltage signals are extracted through Discrete Fourier Transform (DFT) and are normalized by a transformation. The proposed fault locator, which is designed based on Random Forests (RF) algorithm, is trained based on these normalized harmonic components. RF algorithm has the capability of learning patterns with a large number of features. The proposed approach only requires voltage signals measured at one-end hence, there are not problems of transmitting and synchronization of two-end data. In addition, current measurement is not required and the proposed approach is sheltered against current transformer errors and its saturation. No need for very high sampling frequency is another advantage of the proposed approach. Numerous tests carried out on a sample system indicate that accuracy of the proposed fault locator is secure against changing fault location, fault inception angle, fault resistance, and magnitude and direction of pre-fault load current. An average of 0.11% is obtained for the fault locating test errors.
A. Bahmanyar, H. Borhani-Bahabadi, S. Jamali,
Volume 16, Issue 3 (9-2020)
Abstract
To realize the self-healing concept of smart grids, an accurate and reliable fault locator is a prerequisite. This paper presents a new fault location method for active power distribution networks which is based on measured voltage sag and use of whale optimization algorithm (WOA). The fault induced voltage sag depends on the fault location and resistance. Therefore, the fault location can be found by investigation of voltage sags recorded throughout the distribution network. However, this approach requires a considerable effort to check all possible fault location and resistance values to find the correct solution. In this paper, an improved version of the WOA is proposed to find the fault location as an optimization problem. This optimization technique employs a number of agents (whales) to search for a bunch of fish in the optimal position, i.e. the fault location and its resistance. The method is applicable to different distribution network configurations. The accuracy of the method is verified by simulation tests on a distribution feeder and comparative analysis with two other deterministic methods reported in the literature. The simulation results indicate that the proposed optimized method gives more accurate and reliable results.
M. Ahmadinia, J. Sadeh,
Volume 17, Issue 4 (12-2021)
Abstract
In this paper, an accurate fault location scheme based on phasor measurement unit (PMU) is proposed for shunt-compensated transmission lines. It is assumed that the voltage and current phasors on both sides of the shunt-compensated line have been provided by PMUs. In the proposed method, the faulted section is determined by presenting the absolute difference of positive- (or negative-) sequence current angles index, firstly. After determining faulted section, the voltage phasor at the shunt-compensator terminal is estimated via the sound section. The faulted section can be assumed as a perfect transmission line that synchronized voltage and current phasors at one end and voltage phasor at the other end are available. Secondly, a new fault location algorithm is presented to locate the precise fault point in the faulted section. In this algorithm, the location of the fault and the fault resistance are calculated simultaneously by solving an optimization problem, utilizing the heuristic Particle Swarm Optimization (PSO) method. The simulation results in MATLAB/SIMULINK platform demonstrate the high performance of the proposed method in finding the fault location in shunt-compensated transmission lines. The proposed scheme has high accuracy for both symmetrical and asymmetrical fault types and high fault resistance.
M. Dodangeh, N. Ghaffarzadeh,
Volume 18, Issue 4 (12-2022)
Abstract
An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the dynamic decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. The results of the currents and voltages norms are applied as features for a topology data analysis algorithm for fault type classifying in the AC microgrid for fault location purposes. The Algorithm was tested on a microgrid that operates with precision equal to 100% in fault classification and a mean error lower than 20 m when forecasting the fault location. The proposed method robustly operates in sampling frequency, fault resistance variation, and noisy and high impedance fault conditions.