A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO)

2010 ◽  
Author(s):  
Rasoul Rahmani ◽  
A. Khairuddin ◽  
Sam M. Cherati ◽  
H. A. Mahmoud Pesaran
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Turbines ◽  
Wind Farm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Novel Method

scholarly journals Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

2021 ◽  
Vol 11 (20) ◽  
pp. 9746
Author(s):  
Menova Yeghikian ◽  
Abolfazl Ahmadi ◽  
Reza Dashti ◽  
Farbod Esmaeilion ◽  
Alireza Mahmoudan ◽  
...  
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Wind Turbines ◽  
Wind Farm ◽  
Particle Swarm ◽  
Primary Objective ◽  
Wake Effect ◽  
Optimal Arrangement ◽  
Swarm Optimization ◽  
Wind Farm Layout

Nowadays, optimizing wind farm configurations is one of the biggest concerns for energy communities. The ongoing investigations have so far helped increasing power generation and reducing corresponding costs. The primary objective of this study is to optimize a wind farm layout in Manjil, Iran. The optimization procedure aims to find the optimal arrangement of this wind farm and the best values for the hubs of its wind turbines. By considering wind regimes and geographic data of the considered area, and using the Jensen’s method, the wind turbine wake effect of the proposed configuration is simulated. The objective function in the optimization problem is set in such a way to find the optimal arrangement of the wind turbines as well as electricity generation costs, based on the Mossetti cost function, by implementing the particle swarm optimization (PSO) algorithm. The results reveal that optimizing the given wind farm leads to a 10.75% increase in power generation capacity and a 9.42% reduction in its corresponding cost.

scholarly journals Parameters Identification of Equivalent Model of Permanent Magnet Synchronous Generator (PMSG) Wind Farm Based on Analysis of Trajectory Sensitivity

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4607
Author(s):  
Jian Zhang ◽  
Mingjian Cui ◽  
Yigang He
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Wind Farm ◽  
Particle Swarm ◽  
System Stability ◽  
Equivalent Model ◽  
Classical Particle ◽  
Measurement Unit ◽  
Swarm Optimization ◽  
Trajectory Sensitivity

As wind farms have great influences on power system stability, it is essential to develop an adaptive as well as robust equivalent model of it. In this paper, a detailed equivalent model of PMSG wind farm and initialization method is developed. The trajectory sensitivity of parameters is analyzed. Then, the key parameters are estimated using improved Genetic Learning Particle Swarm Optimization (GLPSO) hybrid algorithm with phasor measurement unit (PMU). The description and generalization capability, stability for parameter identification of the equivalent model under wake effects, and when some wind turbines are off-line or wind speed is unknown after an event are analyzed. The maximum differences between the values of estimated parameters and their real ones are less than 10% for the proportional magnification constant of DC voltage controller Kp2 and grid side current controller Kp3. The convergence rate and global optimization performance of the improved GLPSO hybrid algorithm is 0.5 times higher than the classical particle swarm optimization algorithm (PSO) and genetic algorithm (GA).

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

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