November 22, 2024
Rahman Dashti

Rahman Dashti

Academic Rank: Associate professor
Address:
Degree: Ph.D in electrical engineering
Phone: +98-7731222752
Faculty: Faculty of Intelligent Systems and Data Science

Research

Title Linear and Nonlinear Fault Location in Smart Distribution Network under Line Parameter Uncertainty
Type Article
Keywords
gradient descent method optimization problem DGs operation modes time-domain fault location method
Journal IEEE Transactions on Industrial Informatics
DOI 10.1109/TII.2021.3067007
Researchers hamid mirshekali (First researcher) , Rahman Dashti (Second researcher) , hamidreza shaaker (Third researcher) , Amin Torabi Jahromi (Fifth researcher)

Abstract

The line parameters of the distribution network (DN) may change because of atmospheric, structural, and operational conditions. The changes of the line parameters and their uncertainty compromise the accuracy of automatic fault location, which is important for distribution grid operators (DSOs). This paper presents a new time-domain fault location (TDFL) method to locate faults in smart power DN under the line parameters uncertainty. Besides, a time-domain distributed model of line parameters is used to enhance parameter estimation accuracy and FL. In the suggested method, line parameters' accurate values are determined using a mixed gradient descent particle swarm optimization (GD-PSO) algorithm. The effects of parameter uncertainty, DGs operation conditions and modes, different types of arc faults, various fault distances, resistances, and the fault inception angles are studied. For further evaluation of the proposed method's robustness, two practical tests in the laboratory are carried out.