Particle Swarm Optimization in Image Processing of Power Flow Learning Distribution

2020 ◽  
Author(s):  
Chuanchao Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Flow ◽  
Integrated Control ◽  
Particle Swarm ◽  
Heating System ◽  
Swarm Optimization ◽  
Power Adjustment ◽  
The Mathematical Model ◽  
Tie Line

Abstract In order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

scholarly journals Particle swarm optimization in image processing of power flow learning distribution

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Chuanchao Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Flow ◽  
Integrated Control ◽  
Particle Swarm ◽  
Heating System ◽  
Swarm Optimization ◽  
Power Adjustment ◽  
The Mathematical Model ◽  
Tie Line

AbstractIn order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

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.

Power Management System of a Particle Swarm Optimization Controlled Grid Integrated Hybrid PV/WIND/FC/Battery Distributed Generation System

2021 ◽  
Author(s):  
T. Praveen Kumar ◽  
N. Subrahmanyam ◽  
Maheswarapu Sydulu
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Management ◽  
Distributed Generation ◽  
Power Flow ◽  
Reactive Power ◽  
Particle Swarm ◽  
System Control ◽  
Swarm Optimization ◽  
Power System Control

In this manuscript, the Power management of grid integrated hybrid distributed generation (DG) system with Particle swarm optimization (PSO) algorithm is proposed. The hybrid DG system combines with photovoltaic, wind turbine, fuel cell, battery. Depending on the use of hybrid sources and the changes of power production the variation of power can occurs in the DG system. The major purpose of the proposed method restrains the power flow (PF) on active with reactive power between the source and grid side. In the power system control the proposed PSO method is utilized to maximize the active with reactive PF and the controllers. The proposed method interact the load requirement energy and maintain the load sensitivity due to charging and discharging battery control. In the DG system, the proposed PSO method allows maximum power flow. To assess the PF, the constraints of equality and inequality have been evaluated and they are utilized to determine the accessibility of renewable energy source (RES), electricity demand, and the storage elements of charge level. The protection of the power system is enhanced based on the proposed PSO method. Additionally, for retaining a stable output the renewable power system and battery is used. The proposed method is activated in MATLAB/Simulink working platform and the efficiency is likened with other existing methods.

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 design and control for UPFC using the evolutionary algorithms

2016 ◽  
Vol 51 (3) ◽  
pp. 231-238
Author(s):  
M Firouzjahi ◽  
A Shokri
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Power System ◽  
Power Flow ◽  
Particle Swarm ◽  
Unified Power Flow Controller ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Hybrid Particle Swarm Optimization ◽  
Flexible Alternating Current Transmission

Among the Unified Power Flow Controller (UPFC) tools, Flexible Alternating Current Transmission Systems (FACTS) have ability to control the transmitted power, improve transient and dynamic stability and improve the profile of the voltage and damping of the oscillations in the power system. Using the proportional-integral (PI) and proportional-integral-derivative (PID) controllers is a custom method. Selecting the PI and PID coefficients is through different methods. Also designing a resistant controller which can control the system in different points of work has been continuously considered by researchers. In this regard, in order to improve the performance of UPFC controllers, adjusting its parameters is required optimally which this matter itself would facilitate accessing to control objectives. In this project, UPFC is used for damping the oscillations of the power system. Also, in order to adjust the controller parameters optimally, evolutionary algorithms like Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO) and other algorithms are used.Bangladesh J. Sci. Ind. Res. 51(3), 231-238, 2016

scholarly journals Particle Swarm Optimization- Artificial Neural Network For Power System Load Flow

2012 ◽  
pp. 180-189
Author(s):  
Avnish Singh ◽  
Shishir Dixit ◽  
L. Srivastava
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Flow ◽  
Flow Problem ◽  
Particle Swarm ◽  
Load Flow ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Bus Voltage

Load flow study is done to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state working condition of a power system. It is important and most frequently carried out study performed by power utilities for power system planning, optimization, operation and control. In this paper a Particle Swarm Optimization Neural Network (PSO-ANN) is proposed to solve load flow problem under different loading/ contingency conditions for computing bus voltage magnitudes and angles of the power system. A multilayered feed-forward neural network is trained by using PSO technique. The results show the effectiveness of the proposed PSO-ANN based approach for solving power flow problem having different loading conditions and single-line outage contingencies in IEEE 14 bus system

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