Abstract

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.

Highlights

  • Wind power has evolved as a promising energy source for sustainable development and a cost-effective alternative to fossil fuels for power generation

  • Wind farm layout design has been classified as a complex optimization problem

  • Due to the complexity of the problem, algorithms of linear or polynomial complexity cannot guarantee optimal or even feasible solutions. This motivates the use of nature-inspired iterative heuristics since these algorithms have proven to very effective in solving complex optimization problems

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Summary

INTRODUCTION

Wind power has evolved as a promising energy source for sustainable development and a cost-effective alternative to fossil fuels for power generation. Despite the availability of a number of commercial software tools for wind farm layout design, serious attention has been paid by researchers to artificial intelligence techniques for the purpose. While the application of PSO in this study is on square shaped wind farm layout design, the algorithm can be applied to other shapes such as circular, rectangular, or even irregular. This can be done by making necessary changes to the problem model which can effectively be incorporated within the PSO algorithm.

WAKE AND COST MODELING
PARTICLE SWARM OPTIMIZATION ALGORITHMS FOR WIND FARM LAYOUT DESIGN
Basic Particle Swarm Optimization Algorithm
Impact of Acceleration Coefficients
Solution Structure
Initialization for Basic Particle Swarm Optimization Algorithm
Constraint Handling
Solution Perturbations
Modified Particle Swarm Optimization Algorithm
RESULTS AND DISCUSSIONS
Effect of acceleration coefficients on basic PSO and modified PSO
Comparison of basic PSO and modified PSO
CONCLUSIONS
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