scholarly journals Control of isolated microgrid based renewable energy generation using PID controller

2021 ◽  
Vol 10 (2) ◽  
pp. 127
Author(s):  
Regad Mohamed Sidi Brahim ◽  
M'hamed Helaimi ◽  
Rachid Taleb
Keyword(s):  
Particle Swarm Optimization ◽  
Renewable Energy ◽  
Pid Controller ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Particle Swarm ◽  
Weather Conditions ◽  
Swarm Optimization ◽  
Pid Controller Tuning ◽  
Renewable Energy Generation

<p>This paper investigates optimal PID controller tuning using particle swarm optimization for frequency control in the microgrid system. The proposed microgrid composed of renewable sources such as wind turbine generation and solar system with diesel engine generator and storage systems such as the battery, flywheel, aqua electrolyze, and fuel cell. The microgrid based on renewable energy sources faces different challenges in operation and stability due to the stochastic nature of solar radiation and wind speed that depend upon the weather conditions. Among these challenges, the frequency and power deviations are affected by the sudden unbalance between generation and load which require a suitable and adequate regulation. The principal objective of this study is to reduce the frequency and power deviation by the use of the PID controller optimized based particle swarm optimization due to its simplicity and flexibility to overcome this kind of issues. The simulation results show the better performances and robustness of the proposed controller against the disturbances in load and generations in comparison to using a genetic algorithm.</p>

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.

Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization

2011 ◽  
Vol 181 (16) ◽  
pp. 3323-3335 ◽  
Author(s):  
S.-Z. Zhao ◽  
M. Willjuice Iruthayarajan ◽  
S. Baskar ◽  
P.N. Suganthan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Controller Tuning ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Pid Controller Tuning

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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