Optimized Placement of Wind Turbines in Farms Using the (PSO)

Subject Areas : Renewable Energy

Shaghayegh Madadpour ¹ , Azadeh Nekooei esfahani ² , Mehdi Banihashemi ³

1 - MSC student, Department of Environmental Engineering and Food industry, Faculty of Civil and Earth Resources Engineering, Central Tehran Branch, Islamic Azad university, Tehran, Iran.
2 - Assistant of Professor, Department of Environmental Engineering and Food industry, Faculty of Civil and Earth Resources Engineering, Central Tehran Branch, Islamic Azad university, Tehran, Iran. *(Corresponding Author)
3 - Assistant of Professor, Department of Electrical Engineering, Faculty of Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Received: 2024-01-08 Accepted : 2024-07-03 Published : 2024-12-21

Keywords: Onshore wind farm, optimal layout, wind turbine, wake effect, wind energy, Particle Swarm Optimization (PSO) algorithm.,

Abstract :

Background and Objective: Currently, one of the acceptable methods for enhancing productivity in wind farms is optimizing turbine placement. This technique is utilized to determine the suitable and optimal positions for wind turbines within a wind farm. The primary objective of this placement is to maximize energy production within the wind farm. In a regular wind farm, turbines are positioned uniformly, symmetrically, and in grid-like patterns or columns. In contrast, in an optimized wind farm, the efficiency of turbine energy harnessing from the wind is enhanced through the identification of the best turbine placement positions.

Material and Methodology: One of the most widely employed approaches for determining the optimal turbine positions in wind farms is the utilization of optimization algorithms. In this research, the placement of two regular and optimized wind farms at the Manjil wind site has been simulated using MATLAB software, considering turbines with a capacity of kilowatts for both types of wind farms.

Findings: The results of the simulation for the regular wind farm indicate an annual production capacity of 1.124×10⁷ kilowatt-hours with a cost factor of 8.41 For the optimized wind farm, a Particle Swarm Optimization (PSO) algorithm has been employed, resulting in an annual production capacity of 1.309 ×10⁷ kilowatts and a cost factor of 7.22 in this scenario.

Discussion and Conclusion: Consequently, this study aimed to demonstrate the impact of turbine placement on the wind farm

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