SIMULTANEOUS ALLOCATION OF MULTIPLE DISTRIBUTED GENERATION AND CAPACITORS IN RADIAL NETWORK USING GENETIC-SALP SWARM ALGORITHM

• Published in: Electronics and electromechanics no. 4; pp. 59 - 66
• Main Authors: Djabali, Chabane, Bouktir, Tarek
• Format: Journal Article
• Language: English
• Published: Kharkiv Department of Electrical Apparatus of National Technical University, Kharkiv Polytechnic Institute 01.01.2020; National Technical University, Ukraine; National Technical University "Kharkiv Polytechnic Institute"
• Subjects: Algorithms; Banks (Finance); Capacitors; Computer networks; Distributed generation; Electronic components industry; genetic algorithm; Genetic algorithms; Genetic research; Hybrid systems; Light levels; Minimum cost; Optimization; Optimization theory; Particle swarm optimization; Radial distribution; Reactive power; real power losses; salp swarm algorithm; Stress concentration; Utilities
• ISSN: 2074-272X; 2309-3404; 2309-3404
• DOI: 10.20998/2074-272X.2020.4.08

Purpose. In recent years, the problem of allocation of distributed generation and capacitors banks has received special attention from many utilities and researchers. The present paper deals with single and simultaneous placement of dispersed generation and capacitors banks in radial distribution network with different load levels: light, medium and peak using genetic-salp swarm algorithm. The developed genetic-salp swarm algorithm (GA-SSA) hybrid optimization takes the system input variables of radial distribution network to find the optimal solutions to maximize the benefits of their installation with minimum cost to minimize the active and reactive power losses and improve the voltage profile. The validation of the proposed hybrid genetic-salp swarm algorithm was carried out on IEEE 34-bus test systems and real Algerian distributed network of Djanet (far south of Algeria) with 112-bus. The numerical results endorse the ability of the proposed algorithm to achieve a better results with higher accuracy compared to the result obtained by salp swarm algorithm, genetic algorithm, particle swarm optimization and the hybrid particle swarm optimization algorithms.