OPTIMIZING WIND FARM LAYOUTS USING HISTORICAL DATA AND COMPUTATIONAL FLUID DYNAMICS: A COMPUTATIONAL FRANDSEN WAKE MODEL AND DIFFERENTIAL EVOLUTION ALGORITHM

https://doi.org/10.5281/zenodo.13772577%20

Authors

  • Osadolor Alexander Osayimwense Center for Sustainable Engineering, School of Computing, Engineering and Digital Technologies, Teesside University, United Kingdom
  • Showole Afeez Olamide Center for Sustainable Engineering, School of Computing, Engineering and Digital Technologies, Teesside University, United Kingdom
  • Asamoah Gideon Akwasi Center for Sustainable Engineering, School of Computing, Engineering and Digital Technologies, Teesside University, United Kingdom
  • Eze Tochukwu Judethaddeus Center for Sustainable Engineering, School of Computing, Engineering and Digital Technologies, Teesside University, United Kingdom

Keywords:

SCADA Data Analysis, Renewable Energy, Sustainability, Wind Turbine Spacing, Wind Turbine Performance, Differential Evolution Algorithm

Abstract

The increasing demand for sustainable energy solutions has heightened the importance of optimizing wind farm layouts to enhance efficiency and energy output. This study investigates the optimization of wind turbine placement using historical SCADA data, computational fluid dynamics (CFD), and Differential Evolution (DE) algorithms. We analyzed a comprehensive wind turbine dataset, which included wind speed, active power, theoretical power curves, and wind direction data. The analysis revealed a direct relationship between wind speed and power output, with discrepancies observed at both low and high wind speeds due to system inefficiencies and turbine power limits. The Frandsen wake model was employed to account for the wake losses, while the Differential Evolution algorithm was used to optimize the turbine positions to develop an optimal layout for 100 turbines within a minimum area of 21.6 km², aiming to minimize the wake effects and maximize the energy production. The results demonstrate that proper turbine alignment and spacing significantly improve the efficiency, allowing the turbines to operate closer to their theoretical maximum efficiency. The findings highlight the critical role of integrating historical wind data with CFD simulations to optimize turbine placement and performance. This work offers a valuable framework for future windfarm designs, emphasizing the benefits of data-driven approaches in maximizing renewable energy generation and operational efficiency

Published

2024-09-17

How to Cite

Osadolor , A. O., Showole , A. O., Asamoah, G. A., & Eze , T. J. (2024). OPTIMIZING WIND FARM LAYOUTS USING HISTORICAL DATA AND COMPUTATIONAL FLUID DYNAMICS: A COMPUTATIONAL FRANDSEN WAKE MODEL AND DIFFERENTIAL EVOLUTION ALGORITHM. SADI International Journal of Science, Engineering and Technology (SIJSET), 11(3), 10–25. https://doi.org/10.5281/zenodo.13772577

Issue

Vol. 11 No. 3 (2024): July-September

Section

Original Peer Review Articles
Copyright & Licensing

Copyright (c) 2024 SADI International Journal of Science, Engineering and Technology (SIJSET)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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