A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow

• Main Authors: Imene Cherki, Abdelkader Chaker, Zohra Djidar, Naima Khalfallah, Fadela Benzergua
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Sustainability
• Subjects: reactive power flow; metaheuristic methods; metaheuristic hybridization; genetic algorithm; particles swarms; electrical network
• Online Access: https://www.mdpi.com/2071-1050/11/14/3862
• ISSN: 2071-1050

In this paper, the problem of the Optimal Reactive Power Flow (ORPF) in the Algerian Western Network with 102 nodes is solved by the sequential hybridization of metaheuristics methods, which consists of the combination of both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). The aim of this optimization appears in the minimization of the power losses while keeping the voltage, the generated power, and the transformation ratio of the transformers within their real limits. The results obtained from this method are compared to those obtained from the two methods on populations used separately. It seems that the hybridization method gives good minimizations of the power losses in comparison to those obtained from GA and PSO, individually, considered. However, the hybrid method seems to be faster than the PSO but slower than GA.