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 A survey of fault prediction and location methods in electrical energy distribution networks
Type Article
Keywords
Fault location Fault prediction Distribution network Smart grid Microgrid
Journal MEASUREMENT
DOI https://doi.org/10.1016/j.measurement.2021.109947
Researchers Rahman Dashti (First researcher) , mohammad daisy (Second researcher) , hamid mirshekali (Third researcher) , hamid reza shaker (Fourth researcher) ,

Abstract

One of the main factors that disrupt reliability and stop energy provision is the fault occurrence in distribution networks. Thus, accurate and fast fault prediction and location in distribution networks are essential for increasing reliability, fast restoration, optimal electrical energy consumption, and customer satisfaction. This study reviews and investigates fault prediction and fault location topics. To this end, the existing methods and views in the context of fault prediction are reviewed first; then, fault location is investigated. This paper investigates various methods, their advantages, disadvantages, technical reports, and patents in conventional distribution networks, smart-grids, and micro-grids. Comparison of this study with other surveys indicates that it is more comprehensive and despite others covers fault prediction. In addition, it includes an up to date review of the methods for distance measurement and fault location considering different network types (AC/DC), presence of DG, communication and automation standards, synchronous and unsynchronous measurement, magnetic measurement, and state estimation-based fault location methods.