Next Article in Journal
Design for Reliability: The Case of Fractional-Slot Surface Permanent-Magnet Machines
Next Article in Special Issue
Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
Previous Article in Journal
A Coupled-Inductor DC-DC Converter with Input Current Ripple Minimization for Fuel Cell Vehicles
Previous Article in Special Issue
Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features
Open AccessArticle

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

1
College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
2
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
3
Anhui Provincial Laboratory of New Energy Utilization and Energy Conservation, Hefei University Technology, Hefei 230009, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(9), 1690; https://doi.org/10.3390/en12091690
Received: 2 April 2019 / Revised: 28 April 2019 / Accepted: 29 April 2019 / Published: 5 May 2019
(This article belongs to the Special Issue Applications of Computational Intelligence to Power Systems)
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. View Full-Text
Keywords: particle swarm optimization; particle update mode; inertia weight; reactive power optimization particle swarm optimization; particle update mode; inertia weight; reactive power optimization
Show Figures

Figure 1

MDPI and ACS Style

Jiang, F.; Zhang, Y.; Zhang, Y.; Liu, X.; Chen, C. An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization. Energies 2019, 12, 1690.

AMA Style

Jiang F, Zhang Y, Zhang Y, Liu X, Chen C. An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization. Energies. 2019; 12(9):1690.

Chicago/Turabian Style

Jiang, Fengli; Zhang, Yichi; Zhang, Yu; Liu, Xiaomeng; Chen, Chunling. 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization" Energies 12, no. 9: 1690.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop