Applications of Cloud Model Migration Particle Swarm Optimization and Gaussian Penalty Function in Reactive Power Optimization

Abstract:

In order to cope with the defects of traditional particle swarm optimization (PSO) algorithm, such as its prematurity and deficiency in global optimization, a cloud model migration particle swarm optimization (CMMPSO) algorithm is proposed. Firstly, the X-condition generator based on Cloud model is introduced to adjust the inertia weights of particles; then migration action is implemented to lead the flight of global optimal particle. In allusion to the mixed integer programming problem of reactive power optimization, discrete variables are treated as continuous variables in early iterations, and a discretization operation based on Gaussian penalty function is conducted in later stages. Taking the minimum network loss and minimum voltage offset as objective functions, simulations of IEEE 30-bus system is performed to verify the feasibility and effectiveness of the proposed algorithm.

• Periodical: Advanced Materials Research (Volumes 986-987)
• Pages: 1365-1369
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.986-987.1365
• Online since: July 2014
• Authors: Qing Ma*, Zhi Jun Long, Chang Hong Deng, Miao Li, Jia Bin You, Yong Xiao
• Keywords: Cloud Model, Gaussian Penalty Function, Migration Operation, Particle Swarm Optimization (PSO), Reactive Power Optimization

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