scholarly journals TUNING PID CONTROLLER FOR LOAD FREQUENCY CONTROL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

2006 ◽  
Vol 5 (5) ◽  
pp. 1-9
Author(s):  
H. Mostafa
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Frequency Control ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Load Frequency Control ◽  
Swarm Optimization ◽  
Load Frequency ◽  
Particle Swarm Optimization Technique

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.

scholarly journals Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

2021 ◽  
Vol 13 (19) ◽  
pp. 10728
Author(s):  
Reza Alayi ◽  
Farhad Zishan ◽  
Seyed Reza Seyednouri ◽  
Ravinder Kumar ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Controller Design ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Particle Swarm ◽  
Load Frequency Control ◽  
Swarm Optimization ◽  
Load Frequency ◽  
Frequency Controller

This article studied the load frequency control (LFC) of a multi-source microgrid with the presence of renewable energy sources. To maintain a sustainable power supply, the frequency of the system must be kept constant. A Proportional–Integral–Derivative (PID) controller is presented as a secondary controller to control the frequency of the microgrid in island mode, and the integral of squared time multiplied by error squared (ISTES) is used as a performance index. The use of the Craziness-Based Particle Swarm Optimization (CRPSO), which is an improved version of Particle Swarm Optimization (PSO), improves the convergence speed in optimizing the nonlinear problem of load and frequency controller design. The test microgrid is composed of the load and distributed generation units such as diesel generators, photovoltaics and wind turbines. The proposed controller provided the desired response to adjusting the microgrid frequency, achieving the final response after a short time and making it more stable and less oscillatory compared with the conventional system.

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

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