Frequency Control in Microgrid Power System with Renewable Power Generation Using PID Controller Based on Particle Swarm Optimization

2019 ◽  
pp. 3-13
Author(s):  
M. Regad ◽  
M. Helaimi ◽  
R. Taleb ◽  
Ahmed M. Othman ◽  
Hossam A. Gabbar
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Pid Controller ◽  
Frequency Control ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Renewable Power ◽  
Renewable Power Generation

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.

A Hybrid PSO-LEVY Flight Algorithm Based Fuzzy PID Controller for Automatic Generation Control of Multi Area Power Systems

2017 ◽  
Vol 6 (2) ◽  
pp. 42-63 ◽  
Author(s):  
Ajit Kumar Barisal ◽  
Tapas Kumar Panigrahi ◽  
Somanath Mishra
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Pid Controller ◽  
Particle Swarm ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Generation Control

This article presents a hybrid PSO with Levy flight algorithm (LFPSO) for optimization of the PID controllers and employed in automatic generation control (AGC) of nonlinear power system. The superiority of the proposed LFPSO approach has been demonstrated with comparing to recently published Lozi map-based chaotic optimization algorithm (LCOA) and Particle swarm optimization to solve load-frequency control (LFC) problem. It is found that the proposed LFPSO method has robust dynamic behavior in terms of settling times, overshoots and undershoots by varying the system parameters and loading conditions from their nominal values as well as size and locations of disturbance. Secondly, a three-area thermal power system is considered with nonlinear as Generation Rate Constraints (GRC) and outperforms to the results of Bacteria Foraging algorithm based integral controller as well as hybrid Differential Evolution and Particle Swarm Optimization based fuzzy PID controller for the similar power system. Finally, the proficiency of the proposed controller is also verified by random load patterns.

scholarly journals WIPSO, PSO and GA Techniques to Locate UPFC Effectively in Power System to Improve Voltage Stability and Reduce Losses

2019 ◽  
Vol 8 (2) ◽  
pp. 2000-2006
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Unified Power Flow Controller ◽  
Voltage Profile ◽  
Electrical Power System ◽  
Swarm Optimization ◽  
Facts Devices ◽  
Enhanced Particle Swarm Optimization

The domestic and industrial demand for electricity has been increasing extensively making the power system more expensive. With this increases in demand for electricity, the losses also increase in demand for electricity the losses also increase from power generation to distribution Flexible alternating currents transmission system (FACTS) is used to maintain flexible operation of the power system from power generation level to the distribution level. The reliability of the network system can be enhanced by using FACTs devices in the power system more reliably, the inventions in the advanced power electronics devices can be implemented in the design of FACTs, serices, shunt, serices-shunt and shunt-shunt are some of the FACTs devices. One way to operate the power system with less power losses and improved system voltage profile is to use FACTs. Unified power flow controller (UPFC) is one of the serices-shunt FACTs systems. This paper throws light on how UPFC can be used to improve the voltage profile and reduce the installation cost of UPFC, the system loss, in the electrical power system. Analyst and soft computing methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Weight Enhanced Particle Swarm Optimization (WIPSO) are used to determine ideal FACTs device settings and FACTs device place

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>

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