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Research on Modification and Application of Particle Swarm Optimization Algorithm Based on Control Methods

Author: LingJun
Tutor: HeDaKuo
School: Northeastern University
Course: Control Theory and Control Engineering
Keywords: Particle Swarm Optimization Convergence Fuzzy PID controller Power system stabilizer Diversity
CLC: TP301.6
Type: Master's thesis
Year: 2009
Downloads: 34
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Particle Swarm Optimization advantage of groups looking for the solution of complex problems as a kind of swarm intelligence algorithm, provides a new way of thinking, so the study and grasp the characteristics and laws of a theory and application of the significance of the two aspects of the subject at the same time expand their applications have important practical significance. In this paper, control methods and ideas, from the convergence of the algorithm, improved application of its study, the details are as follows: (1) the introduction of the particle swarm algorithm. This paper describes the Improvement and Application Research of the particle swarm optimization of the basic principles and algorithmic process, a comprehensive overview of the particle swarm algorithm, and from multifaceted compare particle swarm optimization algorithm and the similarities and differences of the evolutionary algorithm. (2) analyze the convergence of the particle swarm algorithm. In detail two typical particle the convergence trajectory analysis method based on the application of signal flow graph with Julius stability criterion in pbest and gbest dynamic changes to conditions on particle swarm optimization convergence analysis, and has been The more reasonable algorithm the convergence parameter range, the simulation results demonstrate the reasonableness of the conclusions of the convergence of this article. (3) particle swarm algorithm based on fuzzy PID controller. Difference model of the particle swarm algorithm, the basic particle swarm algorithm can be regarded as a pair of input single-output feedback system. On this basis, this paper proposes a combination of fuzzy PID controller in control theory and particle swarm optimization the mixed algorithm - fuzzy PID-PSO algorithm. The numerical simulation results show that the fuzzy PID-PSO balance algorithm necessary to develop the local capacity and global ability to open up, to verify the validity of the method. Power system stabilizer (PSS) fuzzy PID-PSO is applied to the Anderson system parameter optimization problems, eigenvalue analysis shows that the PSS after fuzzy PID-PSO algorithm to optimize the parameters significantly improved the stability of the system. (4) particle swarm algorithm based on the diversity of feedback. Diversity is a key factor of the particle swarm algorithm for global optimization performance, pursuant to which, the new the diversity evaluation system with quantitative methods, and the feedback control mechanism into particle swarm algorithm is proposed based on the improved diversity feedback Particle swarm optimization (DPSO). The numerical experiments show that the the DPSO algorithm has better global convergence performance. DPSO algorithm is applied to high-Restoration England system PSS parameter optimization problem, and further verify the effectiveness of the method.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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