z-logo
open-access-imgOpen Access
Optimal fuzzy load frequency controller with simultaneous auto-tuned membership functions and fuzzy control rules
Author(s) -
AbbasAli Zamani,
Ehsan Bijami,
Farid Sheikholeslam
Publication year - 2014
Publication title -
turkish journal of electrical engineering and computer sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 30
eISSN - 1303-6203
pISSN - 1300-0632
DOI - 10.3906/elk-1203-3
Subject(s) - control theory (sociology) , settling time , fuzzy logic , robustness (evolution) , fuzzy control system , parametric statistics , electric power system , automatic frequency control , control engineering , controller (irrigation) , computer science , step response , engineering , power (physics) , mathematics , control (management) , artificial intelligence , telecommunications , agronomy , biology , biochemistry , chemistry , statistics , physics , quantum mechanics , gene
In this paper, an auto-tuned fuzzy load frequency controller (FLFC)-based articial bee colony (ABC) algorithm is developed to quench the deviations in the frequency and tie-line power due to load disturbances in an interconnected power system. Optimal tuning of membership functions (MFs) and fuzzy control rules is very important to improve the design performance and achieve a satisfactory level of robustness for a particular operation. In this work, to reduce the fuzzy system design eort and take large parametric uncertainties into account, a new systematic and simultaneous tuning method is developed for designing MFs and fuzzy rules. For this, the designing problem is restructured as an optimization problem and the ABC algorithm is employed to solve it. This newly developed method provides some advantages such as a exible controller with a simple structure and easy algorithm. For the purpose of the proposed method's evaluation, the designed controller is applied to a 2-area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic proportional-integral controller. Simulation results show better operation and improved system parameters, such as the settling time and step response rise time, using the proposed approach, in the presence of system parameter variations.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom