scholarly journals The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design

2020 ◽  
Vol 48 (4) ◽  
pp. 922-930
Author(s):  
Shafiqur Rehman ◽  
Salman Khan ◽  
Luai Alhems
Keyword(s):  
Particle Swarm Optimization ◽  
Design Problem ◽  
Wind Farm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Layout Design ◽  
Energy Output ◽  
Swarm Optimization ◽  
Wind Farm Layout ◽  
The Impact

Wind energy has become a strong alternative to traditional sources of energy. One important decision for an efficient wind farm is the optimal layout design. This layout governs the placement of turbines in a wind farm. The inherent complexity involved in this process results in the wind farm layout design problem to be a complex optimization problem. Particle Swarm Optimization (PSO) algorithm has been effectively used in many studies to solve the wind farm layout design problem. However, the impact of an important set of PSO parameters, namely, the acceleration coefficients, has not received due attention. Considering the importance of these parameters, this paper presents a preliminary analysis of PSO acceleration coefficients using the conventional and a modified variant of PSO when applied to wind farm layout design. Empirical results show that the acceleration coefficients do have an impact on the quality of final layout, resulting in better overall energy output.

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

2014 ◽  
Vol 1070-1072 ◽  
pp. 297-302
Author(s):  
Zhi Kui Wu ◽  
Chang Hong Deng ◽  
Yong Xiao ◽  
Wei Xing Zhao ◽  
Qiu Shi Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Real Time ◽  
Wind Power ◽  
Wind Farm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Proposed Model

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

scholarly journals Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Prabha Umapathy ◽  
C. Venkataseshaiah ◽  
M. Senthil Arumugam
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Optimal Power ◽  
The Impact

This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO). The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW), a time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.

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 Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

2017 ◽  
Vol 24 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Qin Zheng ◽  
Zubin Yang ◽  
Jianxin Sha ◽  
Jun Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Extreme Values ◽  
Initial Perturbation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Prediction Time ◽  
The Impact

Abstract. In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

scholarly journals On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

2006 ◽  
Vol 2006 ◽  
pp. 1-17 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Single Stage ◽  
Hybrid Manufacturing ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Real Coded Genetic Algorithm ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

Optimal Design of an Autotensioner in an Automotive Belt Drive System Via a Dynamic Adaptive PSO-GA

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Hao Zhu ◽  
Yumei Hu ◽  
W. D. Zhu ◽  
Yangjun Pi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Design Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Drive System ◽  
Penalty Function Method ◽  
Belt Drive ◽  
Original Design ◽  
Swarm Optimization

Noise, vibration, and harshness performances are always concerns in design of an automotive belt drive system. The design problem of the automotive belt drive system requires the minimum transverse vibration of each belt span and minimum rotational vibrations of each pulley and the tensioner arm at the same time, with constraints on tension fluctuations in each belt span. The autotensioner is a key component to maintain belt tensions, avoid belt slip, and absorb vibrations in the automotive belt drive system. In this work, a dynamic adaptive particle swarm optimization and genetic algorithm (DAPSO-GA) is proposed to find an optimum design of an autotensioner to solve this design problem and achieve design targets. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization (PSO) algorithm to balance the convergence rate and global optimum search ability by adaptively adjusting the search velocity during the search process. genetic algorithm (GA)-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the PSO algorithm to further exploit optimal solutions generated by the PSO algorithm. These operators are used to diversify the swarm and prevent premature convergence. The objective function is established using a weighted-sum method, and the penalty function method is used to deal with constraints. Optimization on an example automotive belt drive system shows that the system vibration is greatly improved after optimization compared with that of its original design.

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