Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization

被引:171
作者
Panda, S. [1 ]
Sahu, B. K. [2 ]
Mohanty, P. K. [2 ]
机构
[1] VSSUT, Dept Elect & Elect Engn, Burla 768018, Odisha, India
[2] SOA Univ, ITER, Dept Elect Engn, Bhubaneswar 751030, Odisha, India
来源
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2012年 / 349卷 / 08期
关键词
GENETIC ALGORITHM; STRATEGY;
D O I
10.1016/j.jfranklin.2012.06.008
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PI D controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of -50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:2609 / 2625
页数:17
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