A Design of PI Controller using Stochastic Particle Swarm Optimization in Load Frequency Control of Thermal Power Systems

2015 ◽  
Author(s):  
K. Jagatheesan ◽  
B. Anand ◽  
Nillanjan Dey ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Frequency Control ◽  
Thermal Power ◽  
Particle Swarm ◽  
Pi Controller ◽  
Load Frequency Control ◽  
Swarm Optimization ◽  
Load Frequency ◽  
Stochastic Particle

Load Frequency Control Based on Modern Techniques in Two Areas Power Systems

2018 ◽  
pp. 163-191 ◽  
Author(s):  
Naglaa Kamel Bahgaat ◽  
Mohamed Ibrahim ◽  
Mohamed Ahmed Moustafa Hassan ◽  
Fahmy M. A. Bendary
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Frequency Control ◽  
Particle Swarm ◽  
Problem Formulation ◽  
Load Frequency Control ◽  
New Technique ◽  
Swarm Optimization ◽  
Load Frequency ◽  
Adaptive Weight

This chapter gives introduction to evolutionary techniques. Then it presents the problem formulation for load frequency control with evolutionary particle swarm optimization. It gives the application of particle swarm optimization (PSO) in load frequency control; also, it illustrates the use of an adaptive weight particle swarm optimization (AWPSO) and adaptive accelerated coefficients based PSO (AACPSO). Furthermore, it introduces a new modification for AACPSO technique (MAACPSO). The new technique is explained. A well-done comparison will be given in this chapter for these above-mentioned techniques. A reasonable discussion on the obtained results will be displayed. The obtained results are promising.

Load Frequency Control in Power System via Improving PID Controller Based on Particle Swarm Optimization and ANFIS Techniques

2015 ◽  
pp. 462-481 ◽  
Author(s):  
Naglaa K. Bahgaat ◽  
M. I. El-Sayed ◽  
M. A. Moustafa Hassan ◽  
F. A. Bendary
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Pid Controller ◽  
Frequency Control ◽  
Particle Swarm ◽  
Load Frequency Control ◽  
Swarm Optimization ◽  
Load Frequency ◽  
System Frequency ◽  
Tie Line

The main objective of Load Frequency Control (LFC) is to regulate the power output of the electric generator within an area in response to changes in system frequency and tie-line loading. Thus the LFC helps in maintaining the scheduled system frequency and tie-line power interchange with the other areas within the prescribed limits. Most LFCs are primarily composed of an integral controller. The integrator gain is set to a level that compromises between fast transient recovery and low overshoot in the dynamic response of the overall system. This type of controller is slow and does not allow the controller designer to take into account possible changes in operating conditions and non-linearities in the generator unit. Moreover, it lacks robustness. This paper studies LFC in two areas power system using PID controller. In this paper, PID parameters are tuned using different tuning techniques. The overshoots and settling times with the proposed controllers are better than the outputs of the conventional PID controllers. This paper uses MATLAB/SIMULINK software. Simulations are done by using the same PID parameters for the two different areas because it gives a better performance for the system frequency response than the case of using two different sets of PID parameters for the two areas. The used methods in this paper are: a) Particle Swarm Optimization, b) Adaptive Weight Particle Swarm Optimization, c) Adaptive Acceleration Coefficients based PSO (AACPSO) and d) Adaptive Neuro Fuzzy Inference System (ANFIS). The comparison has been carried out for these different controllers for two areas power system. Therefore, the article presents advanced techniques for Load Frequency Control. These proposed techniques are based on Artificial Intelligence. It gives promising results.

Load Frequency Control in Power System via Improving PID Controller Based on Particle Swarm Optimization and ANFIS Techniques

2014 ◽  
Vol 3 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Naglaa K. Bahgaat ◽  
M. I. El-Sayed ◽  
M. A. Moustafa Hassan ◽  
F. A. Bendary
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Pid Controller ◽  
Frequency Control ◽  
Particle Swarm ◽  
Load Frequency Control ◽  
Swarm Optimization ◽  
Load Frequency ◽  
System Frequency ◽  
Tie Line

The main objective of Load Frequency Control (LFC) is to regulate the power output of the electric generator within an area in response to changes in system frequency and tie-line loading. Thus the LFC helps in maintaining the scheduled system frequency and tie-line power interchange with the other areas within the prescribed limits. Most LFCs are primarily composed of an integral controller. The integrator gain is set to a level that compromises between fast transient recovery and low overshoot in the dynamic response of the overall system. This type of controller is slow and does not allow the controller designer to take into account possible changes in operating conditions and non-linearities in the generator unit. Moreover, it lacks robustness. This paper studies LFC in two areas power system using PID controller. In this paper, PID parameters are tuned using different tuning techniques. The overshoots and settling times with the proposed controllers are better than the outputs of the conventional PID controllers. This paper uses MATLAB/SIMULINK software. Simulations are done by using the same PID parameters for the two different areas because it gives a better performance for the system frequency response than the case of using two different sets of PID parameters for the two areas. The used methods in this paper are: a) Particle Swarm Optimization, b) Adaptive Weight Particle Swarm Optimization, c) Adaptive Acceleration Coefficients based PSO (AACPSO) and d) Adaptive Neuro Fuzzy Inference System (ANFIS). The comparison has been carried out for these different controllers for two areas power system. Therefore, the article presents advanced techniques for Load Frequency Control. These proposed techniques are based on Artificial Intelligence. It gives promising results.

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