November 1, 2024

Dariush Keihan Asl

Academic Rank: Assistant professor
Address: Faculty of Intelligent Systems Engineering and Data Science
Degree: Ph.D in Electrical Engineering (Power)
Phone: 0
Faculty: Faculty of Intelligent Systems and Data Science

Research

Title Holomorphic Embedding Load Flow for Unbalanced Radial Distribution Networks with DFIG and Tap-Changer Modelling
Type Article
Keywords
Holomorphic-Embedding Method, Three-Phase Unbalanced Radial Distribution Networks, Load Flow Problem, Current Injection Vector
Journal IET Generation Transmission & Distribution
DOI https://doi.org/10.1049/iet-gtd.2018.6239
Researchers Dariush Keihan Asl (First researcher) , Mohammad Mohammadi (Second researcher) , Ali Reza Seifi (Third researcher)

Abstract

In this study, the holomorphic-embedding method as a linear and non-iterative method is developed and implemented to solve the load flow of the three-phase unbalanced radial distribution networks. Previously, this method was used to solve the load flow problem of balanced transmission networks. The proposed methodology could be applied for a variety of unbalanced radial distribution networks with any number of buses and branches. The characteristics of unbalanced radial distribution networks including line coupling effects, unbalanced loads and unsymmetrical phases are considered in this study. The proposed method uses an unbalanced network structure and modifies the calculation approach of the admittance matrix. In this manner, an unbalanced network is modelled the same as the balanced networks. The tap-changer, doubly-fed induction generator (DFIG), and photovoltaic system are modelled in the admittance matrix or current injection vector. The performance of the proposed method is validated on the 19-bus unbalanced radial distribution network. Finally, the effectiveness of the proposed method in solving the load flow problem is investigated.