TY  - BOOK
AU  - Mugerwa ,Geofrey
AU  - Abdelkader ,Sobby
AU  - Elsabrouty ,Maha
AU  - Megahed ,Tamer
AU  - Asano ,Tanemasa
AU  - Abdelkader ,Sobby
AU  - Abdelazim ,Mohamed
AU  - Nasrat ,Loay Saad Eldin
AU  - عبدالقادر ,صبحي
AU  - الصبروتي ,مها
AU  - مجاهد ,تامر
AU  - اسانو ,تانيماسا
AU  - عبدالقادر ,صبحي
AU  - عبدالعظيم ,محمد عبدالعظيم محمد
AU  - نصرت ,لؤي سعد الدين
TI  - Data - Driven Customer - Phase Relationship Identification in Low - Voltage Distribution Networks with Distributed Generations : : A Thesis Submitted to the Graduate School of Electronics , Communication , and Computer Engineering : Egypt-Japan University of Science and Technology (E-JUST) : In Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical Power Engineering / 
AV  - EPE MSc. 2024 04 04
PY  - 2024///
CY  - Alexandria : 
PB  - Geofrey Mugerwa
N1  - Includes a title page in Arabic; Thesis (M.Sc.)
; Issued also as a digital file (for more information please check our Digital Repository)
N2  - Phase identification involves determining which phase each end-customer is connected to in a multi-phase distribution network Knowing the correct customer-phase connectivity provides Distribution System Operators (DSOs) with critical information for efficient operation and management of Low-Voltage Distribution Networks (LVDNs) Additionally , accurate phase connectivity information is essential for increasing the hosting capacity of Distributed Energy Resources (DERS) , which are key players in modern power systems On the other hand , incorrect customer-phase connectivity increases the risk of phase unbalance , leading to nuisance tripping of protection devices and equipment damage for assets such as transformers Usually , the phase connectivity information of a LVDN is known to DSOs at the time of planning and commissioning the power line However , due to the complexity and the various changes that occur during its operation , such as new customer connections , maintenance and repair operations , cyber attacks , emergency restoration services , etc. , the original data files maintained at the power management center often contain false connectivity information This challenge is traditionally addressed by applying hardware-based phase identification techniques Nevertheless, such approaches are costly , prone to human errors , and time - consuming, as they involve either installing expensive high-precision devices or employing field-based methods To overcome the above challenges , this thesis develops a novel data- driven method to identify the phase connectivity of end-customers using Advanced Metering Infrastructure (AMI) voltage and current measurements , collected every 15 minutes Firstly , a preprocessing method that employs linear interpolation and singular value decomposition is adopted to improve the quality of the smart meter data Secondly , using Kirchoff's current law and correlation analysis , a 0-1 linear integer programming optimization model is built to uniquely identify the phase to which each customer is connected The datasets utilized are obtained by performing power flow simulations on a modified IEEE-906 test system using OpenDSS software The robustness of the proposed identification algorithm is tested against dataset size , missing data , measurement errors , and the influence of rooftop photovoltaic generation systems To explore the benefits associated with knowing the correct phase identification information
ER  -