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Lookup NU author(s): Dr Anurag Sharma
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© 2023 Elsevier B.V. The analysis of the distribution system in the presence of distributed generations (DGs) with conventional power flow tools poses a technical challenge owing to higher resistance to reactance ratio, unbalanced phases, and topological structures such as radial and meshed configuration. Therefore, there is a need to explore power flow algorithms for active distribution system with minimum data preparation. This paper reaps the maximum capability of the “Direct Load Flow (DLF)” algorithm and proposes the AC-DC multi-phase power flow algorithms for an unbalanced active distribution system. This paper describes the mathematical modelling of distribution system components and power electronic converters for multi-phase power flow. In this work, computationally efficient multi-phase power flow algorithms are developed using particle swarm optimization (PSO) and quadratic programming (QP). The proposed algorithms can handle power electronics converters operating in various modes such as constant duty ratio, constant modulation index, constant voltage, and constant power. It is also shown that the proposed algorithms are used to solve power flow in radial and weakly meshed AC-DC distribution system. The proposed power flow algorithms are implemented on a 13 node balanced distribution system, modified IEEE 13 node unbalanced distribution network, and a modified IEEE 123 node unbalanced AC-DC distribution system. Further, the proposed power flow algorithms are compared with MATLAB Simulink and DistFlow method. It is inferred from the results that PSO and QP based distribution system power flow algorithms incorporating DLF matrices are robust, computationally efficient, and scalable.
Author(s): Bhattar PL, Pindoriya NM, Sharma A, Naayagi RT
Publication type: Article
Publication status: Published
Journal: Electric Power Systems Research
Year: 2024
Volume: 226
Print publication date: 01/01/2024
Online publication date: 31/10/2023
Acceptance date: 09/10/2023
ISSN (print): 0378-7796
ISSN (electronic): 1873-2046
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.epsr.2023.109924
DOI: 10.1016/j.epsr.2023.109924
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