An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization

Fengli Jiang, Yichi Zhang, Yu Zhang, Xiaomeng Liu and Chunling Chen
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Fengli Jiang: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yichi Zhang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yu Zhang: Anhui Provincial Laboratory of New Energy Utilization and Energy Conservation, Hefei University Technology, Hefei 230009, China
Xiaomeng Liu: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Chunling Chen: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

Energies, 2019, vol. 12, issue 9, 1-14

Abstract: An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative particles and chaos particles were introduced into the population of the developed algorithm, which was to decrease the randomness and promote search efficiency through guiding particle position updating. Moreover, the cluster focus distance-changing rate was responsible for dynamically adjusting inertia weight. Then the convergence rate and accuracy of this algorithm would be elevated by four functions, which would test effectively the proposed. Finally, the optimized algorithm was verified on the RPO of the IEEE 30-bus power system. The performance of PSO, Random weight particle swarm optimization (WPSO) and Linearly decreasing weight of the particle swarm optimization algorithm (LDWPSO) were identified as the referential information, the proposed GSAPSO was more efficient from the comparison. Calculation results demonstrated that higher quality solutions were obtained and convergence rate and accuracy was significantly higher with regard to the GSAPSO algorithm.

Keywords: particle swarm optimization; particle update mode; inertia weight; reactive power optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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