Genetic Algorithm-Based Fuzzy Controller for Improving the Dynamic Performance of Self-Excited Induction Generator

2012 ◽  
Vol 37 (3) ◽  
pp. 665-682 ◽  
Author(s):  
Abdel-Fattah Attia ◽  
Yusuf. A. Al-Turki ◽  
Hussein F. Soliman
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Controller ◽  
Dynamic Performance ◽  
Induction Generator

scholarly journals Genetic Algorithm Based Control System Design of a Self-Excited Induction Generator

10.14311/812 ◽  
2006 ◽  
Vol 46 (2) ◽  
Author(s):  
A.-F. Attia ◽  
H. Soliman ◽  
M. Sabry
Keyword(s):  
Genetic Algorithm ◽  
Power Control ◽  
Reactive Power ◽  
Dynamic Performance ◽  
Input Power ◽  
The Self ◽  
Induction Generator ◽  
Reactive Control ◽  
Wind Energy Conversion ◽  
Terminal Voltage

This paper presents an application of the genetic algorithm (GA) for optimizing controller gains of the Self-Excited Induction Generator (SEIG) driven by the Wind Energy Conversion Scheme (WECS). The proposed genetic algorithm is introduced to adapt the integral gains of the conventional controllers of the active and reactive control loop of the system under study, where GA calculates the optimum value for the gains of the variables based on the best dynamic performance and a domain search of the integral gains. The proposed genetic algorithm is used to regulate the terminal voltage or reactive power control, by adjusting the self excitation, and to control the mechanical input power or active power control by adapting the blade angle of WECS, in order to adjust the stator frequency. The GA is used for optimizing these gains, for an active and reactive power loop, by solving the related optimization problem. The simulation results show a better dynamic performance using the GA than using the conventional PI controller for active and reactive control.

scholarly journals Fuzzy Algorithm for Supervisory Voltage/Frequency Control of a Self Excited Induction Generator

10.14311/898 ◽  
2006 ◽  
Vol 46 (6) ◽  
Author(s):  
Hussein F. Soliman ◽  
Abdel-Fattah Attia ◽  
S. M. Mokhymar ◽  
M. A. L. Badr
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Frequency Control ◽  
Dynamic Performance ◽  
Pi Controller ◽  
Induction Generator ◽  
Wind Energy Conversion ◽  
Voltage Frequency ◽  
Terminal Voltage ◽  
Rising Time

This paper presents the application of a Fuzzy Logic Controller (FLC) to regulate the voltage of a Self Excited Induction Generator (SEIG) driven by Wind Energy Conversion Schemes (WECS). The proposed FLC is used to tune the integral gain (KI) of a Proportional plus Integral (PI) controller. Two types of controls, for the generator and for the wind turbine, using a FLC algorithm, are introduced in this paper. The voltage control is performed to adapt the terminal voltage via self excitation. The frequency control is conducted to adjust the stator frequency through tuning the pitch angle of the WECS blades. Both controllers utilize the Fuzzy technique to enhance the overall dynamic performance.  The simulation result depicts a better dynamic response for the system under study during the starting period, and the load variation. The percentage overshoot, rising time and oscillation are better with the fuzzy controller than with the PI controller type. 

Deadbeat–fuzzy controller for the power control of a Doubly Fed Induction Generator based wind power system

2019 ◽  
Vol 88 ◽  
pp. 258-267 ◽  
Author(s):  
C.M. Rocha-Osorio ◽  
J.S. Solís-Chaves ◽  
Lucas L. Rodrigues ◽  
J.L. Azcue Puma ◽  
A.J. Sguarezi Filho
Keyword(s):  
Power System ◽  
Power Control ◽  
Wind Power ◽  
Fuzzy Controller ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Wind Power System ◽  
Doubly Fed

Design and Simulation of Temperature Control System in Intermittent Reaction Kettles Based on Intelligent Fuzzy Control

2013 ◽  
Vol 380-384 ◽  
pp. 294-297 ◽  
Author(s):  
Xin Wei Li
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Pid Control ◽  
Elevated Temperatures ◽  
Fuzzy Controller ◽  
Dynamic Performance ◽  
Temperature Curve ◽  
Time Variable ◽  
Temperature Control System ◽  
New Type

A temperature rising control system and temperature maintaining control system were designed in according to time-variable and hysteretic nature of temperature change and limitation when traditional PID control deals with nonlinear systems. A new type of intelligent fuzzy controller combination of traditional PID control and fuzzy control was designed and applied in temperature maintaining control system. The simulation results show that the holding phase at elevated temperatures and temperature, the temperature curve has a high steady-state accuracy and dynamic performance in the period of temperature rising and maintaining, and the system and controller cause a better result.

Application of genetic algorithm to self-organizing fuzzy controller in fed-batch culture ofScutellaria baicalensis G. plant cell

1998 ◽  
Vol 15 (4) ◽  
pp. 404-410 ◽  
Author(s):  
Jeong-Woo Choi ◽  
Jin Man Cho ◽  
Jung Gun Lee ◽  
Won Hong Lee ◽  
Ik Hwan Kim ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Batch Culture ◽  
Plant Cell ◽  
Fuzzy Controller ◽  
Fed Batch ◽  
Fed Batch Culture ◽  
Self Organizing

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

Sign in / Sign up

Export Citation Format

Share Document