Online tuning of genetic based PID controller in LFC systems using RBF neural network and VSTLF technique

Title:

Online tuning of genetic based PID controller in LFC systems using RBF neural network and VSTLF technique

Creator:

Monfared, Mohammad, Daryani , Abbas Molavi, and Abedi , Mehrdad

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:0f5c35b8-90bc-4873-aa7e-bf64a7bcded6
uuid:0f5c35b8-90bc-4873-aa7e-bf64a7bcded6

Subject:

Load frequency (LFC), real coded genetic algorithm (RCGA), radial basis function neural network (RBFN), and very short time load forecasting (VSTLF)

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, a novel control strategy for the load frequency control (LFC) system is proposed. The developed method includes a genetic algorithm (GA) based self-tuned PID controller for online application. The new method is presented in order to regulate PID controller coefficients by a radial basis function neural network (RBFN). Furthermore, a very short time load forecasting (VSTLF) scheme is also employed as a novel approach for the system load variations to be considered in the LFC system. For validation of the proposed method, several comparative case studies are presented. The simulation results indicate that the proposed strategy improves the system dynamics remarkably.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2008 Volume:18 | Number:4

Harvested from:

CDK

Metadata only:

false