Journal ArticleDOI
A particle swarm optimization approach for optimum design of PID controller in AVR system
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TLDR
The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.Abstract:
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.read more
Citations
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Journal ArticleDOI
Improving the dynamic response of frequency and power in a wind integrated power system by optimal design of compensated superconducting magnetic energy storage
TL;DR: A substantial improvement in the dynamic response of system frequency and power deviations by utilizing the compensated superconducting magnetic energy storage (CSMES) system with proportional integral derivative (PID) controller is indicated.
A genetically tuned optimal PID controller
TL;DR: In this article, an optimal PID controller is proposed to adjust the parameters of the controller a fitness function in terms of transient, steady state parts of response and control energy characteristics of system is introduced.
Proceedings ArticleDOI
Tuning PID Controller Using Hybrid Genetic Algorithm Particle Swarm Optimization Method for AVR System
TL;DR: In this paper a comparison between algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and Hybrid Genetic Al algorithm Particles Swarmoptimization (HGAPSO) is proposed with their characteristics and performance analysis to find an optimum parameters of the PID controller.
Proceedings ArticleDOI
Optimal design of PIDA controller using firefly algorithm for AVR power system
D. K. Sambariya,D. Paliwal +1 more
TL;DR: A new proportional, integral, derivative and acceleration (PIDA) controller is presented and designed for an automatic voltage regulator power system by considering as an optimization with minimization of square error using firefly algorithm.
Journal ArticleDOI
Improved PSO algorithm and its application
TL;DR: Simulation and practical results show that the global search ability of IPSO is improved greatly and optimization of time-sharing power supply for zinc electrolytic process can bring about outstanding economic benefit for plant.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
Empirical study of particle swarm optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: The experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.
Book
Power System Analysis
TL;DR: This is the first text in this area to fully integrate MATLAB and SIMULINK throughout and provides students with an author-developed POWER TOOLBOX DISK organized to perform analyses and explore power system design issues with ease.
Book
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
TL;DR: In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence and carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution.