Power System Reconfiguration in Distribution Network for Improving Reliability Using Genetic Algorithm and Particle Swarm Optimization
Source abstract
Abstract
This paper presents an optimal method for optimizing network reconfiguration problems in a power distribution system in order to enhance reliability and reduce power losses. Network reconfiguration can be viewed as an optimization problem involving a set of criteria that must be reduced when adhering to various constraints. The energy not supplied (ENS) during permanent network faults and active power losses are the objective functions that are optimized in this study during the reconfiguration phase. These objectives are expressed mathematically and will be integrated into various optimization algorithms used throughout the study. To begin, a mathematical formulation of the objectives to be optimized, as well as all the constraints that must be met, is proposed. Then, to solve this difficult combinatorial problem, we use the exhaustive approach, genetic algorithm (GA), and particle swarm optimization (PSO) on an IEEE 33-bus electrical distribution network. Finally, a performance evaluation of the proposed approaches is developed. The results show that optimizing the distribution network topology using the PSO approach contributed significantly to improving the reliability, node voltage, line currents, and calculation time.
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Research context
OpenAlex topics: Optimal Power Flow Distribution · Power System Reliability and Maintenance · Microgrid Control and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics Engineering, Community College, University of Ha’il, Ha’il 81481, Saudi Arabia
Control & Energies Management (CEM-Lab), National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 2440, Saudi Arabia
Department of Computer Engineering, College of Computer Engineering and sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Department of Industrial Engineering, College of Engineering, University of Ha’il, Ha’il 2440, Saudi Arabia
Photovoltaic and Semiconductor Materials Laboratory, National Engineering School of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
Laboratory of Sciences and Techniques of Automatic Control &Computer Engineering, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
References (28)
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