Capacitor placement of distribution systems using particle swarm optimization approaches

• Published in: International journal of electrical power & energy systems Vol. 64; pp. 839 - 851
• Main Authors: Lee, Chu-Sheng, Ayala, Helon Vicente Hultmann, Coelho, Leandro dos Santos
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2015
• Subjects: Capacitor placement; Capacitors; Chaos theory; Combinatorial analysis; Electric potential; Energy distribution; Gaussian probability distribution; Mathematical models; Particle swarm optimization; Placement; Swarm intelligence; Voltage; Capacitor placement; Chaos theory; Particle swarm optimization; Gaussian probability distribution
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.07.069

•Capacitor placement plays an important role in distribution system planning and operation.•The capacitor placement problem is a combinatorial optimization problem.•This paper presents a new capacitor placement method which employs particle swarm optimization. Capacitor placement plays an important role in distribution system planning and operation. In distribution systems of electrical energy, banks of capacitors are widely installed to compensate the reactive power, reduce the energy loss in system, voltage profile improvement, and feeder capacity release. The capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. Recently, many approaches have been proposed to solve the capacitor placement problem as a mixed integer programming problem. This paper presents a new capacitor placement method which employs particle swarm optimization (PSO) approaches with operators based on Gaussian and Cauchy probability distribution functions and also in chaotic sequences for a given load pattern of distribution systems. The proposed approaches are demonstrated by two examples of application. Simulation results show that the proposed method can achieve an optimal solution as the exhaustive search can but with much less computational time.