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Showing 2 results for Ghaffarzadeh
A New Fast and Accurate Fault Location and Classification Method on MTDC Microgrids Using Current Injection Technique, Traveling-Waves, Online Wavelet, and Mathematical Morphology
M. Dodangeh, N. Ghaffarzadeh,
Volume 16, Issue 2 (June 2020)
Abstract
In this paper, a new fast and accurate method for fault detection, location, and classification on multi-terminal DC (MTDC) distribution networks connected to renewable energy and energy storages presented. MTDC networks develop due to some issues such as DC resources and loads expanding, and try to the power quality increasing. It is important to recognize the fault type and location 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 derivative of the fault current and applying to a mathematical morphology filter, in the Fault time. The type and location of faults determinate using circuit equations and current calculations. DC series and ground arc faults are considered as DC distribution network disturbances. The presented method was tested in an MTDC network 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 accuracy and speed than conventional approaches. This method robustly operates to changing in sampling frequency, fault resistance, and works very well in high impedance fault.

An Intelligent Machine Learning-Based Protection of AC Microgrids Using Dynamic Mode Decomposition
M. Dodangeh, N. Ghaffarzadeh,
Volume 18, Issue 4 (December 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.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.