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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Proceedings ArticleDOI
A GMM-GFM Based Controller for LFC and AVR of a Single Area Power System
Monika Gupta,Smriti Srivastava +1 more
TL;DR: Combined Generalized Fuzzy Model (GFM) and Gaussian Mixture Model (GMM) based controller is used to determine the optimal parameters of Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) system of single area power system.
Journal ArticleDOI
Performance of pid controller of nonlinear system using swarm intelligence techniques
TL;DR: Swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system and it is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods.
Proceedings ArticleDOI
PID controller tuning by using extremum seeking algorithm based on annealing recurrent neural network
Bin Zuo,Yun-an Hu,Jing Li +2 more
TL;DR: A discrete-time extremum seeking algorithm based on annealing recurrent neural network (ESA-ARNN) for auto-tuning of PID controller parameters is proposed, which has better performance than the eight prevalent PID tuning schemes.
Proceedings ArticleDOI
Optimization of the PI controller to improve the dynamic performance of grid-connected photovoltaic system
TL;DR: This study presents comparison with four cases and showed enhancing of Maximum overshot by around 20% and steady state error by around 65 % than manual tuning.
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.
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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.