Multiobjective reconfiguration of unbalanced distribution networks using improved transient search optimization algorithm considering power quality and reliability metrics

Mohana Alanazi; Abdulaziz Alanazi; Ahmad Almadhor; Zulfiqar Ali Memon

doi:10.1038/s41598-022-17881-x

Multiobjective reconfiguration of unbalanced distribution networks using improved transient search optimization algorithm considering power quality and reliability metrics

Sci Rep. 2022 Aug 11;12(1):13686. doi: 10.1038/s41598-022-17881-x.

Authors

Mohana Alanazi¹, Abdulaziz Alanazi², Ahmad Almadhor³, Zulfiqar Ali Memon⁴

Affiliations

¹ Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia. msanazi@ju.edu.sa.
² Department of Electrical Engineering, College of Engineering, Northern Border University, Ar'Ar, 73222, Saudi Arabia.
³ Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
⁴ Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.

Abstract

This paper proposes a new intelligent algorithm named improved transient search optimization algorithm (ITSOA) integrated with multiobjective optimization for determining the optimal configuration of an unbalanced distribution network. The conventional transient search optimization algorithm (TSOA) is improved with opposition learning and nonlinearly decreasing strategies for enhancing the convergence to find the global solution and obtain a desirable balance between local and global search. The multiobjective function includes different objectives such as power loss reduction, enhancement of voltage sag and unbalance, and network energy not supplied minimization. The decision variables of the reconfiguration problem including opened switches or identification of optimal network configuration are determined using ITSOA and satisfying operational and radiality constraints. The proposed methodology is implemented on unbalanced 13-bus and 118-bus networks. The results showed that the proposed ITSOA is capable to find the optimal network configuration for enhancing the different objectives in loading conditions. The results cleared the proposed methodology's good effectiveness, especially in power quality and reliability enhancement, without compromising the different objectives. Comparing ITSOA to conventional TSOA, particle swarm optimization (PSO), gray wolf optimization (GWO), bat algorithm (BA), manta ray foraging optimization (MRFO), and ant lion Optimizer (ALO), and previous approaches, it is concluded that ITSOA in improving the different objectives.