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Analysis of Wind Turbine Distances Using a Novel Techno-Spatial Approach in Complex Wind Farm Terrains

Author

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  • Bukurije Hoxha

    (Faculty of Mechanical Engineering, University of Prishtina “Hasan Prishtina”, 10 000 Prishtina, Kosovo
    Faculty of Mechanical Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia)

  • Igor K. Shesho

    (Faculty of Mechanical Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia)

  • Risto V. Filkoski

    (Faculty of Mechanical Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia)

Abstract

Among the current challenges facing the energy sector is finding environmentally friendly and high-performance forms of energy generation. One such form of energy generation is from the wind. In addition to the fluctuations that cause changes in the generated energy, another factor that significantly affects the overall efficiency of wind farms is the distance between the turbines. In that context, a distance of at least three diameters (3D) onwards is necessary to enable a stable operation. This is more difficult to implement for mountainous terrain due to the terrain configuration’s influence, the turbine units’ positioning, and the mutual influence resulting from their position in the area under consideration. This work investigates the interdependence of the terrain features, the placement of ten turbines in different scenarios, and the impact on the overall efficiency of the wind farm. The place where the wind farm is considered is in Koznica, a mountainous area near Prishtina. An analysis has been carried out for two-diameter (2D), three-diameter (3D), and five-diameter (5D) turbine blade spacing for turbines with a rated power of 3.4 MW. The study considers placement in the following forms: Arc, I, L, M, and V. The results show that for 2D distance layout, the capacity factors for Arc, I, L, M, and V placements have the values: 32.9%, 29.8%, 31.1%, 30.6%, and 37.1%. For the 3D distance, according to these scenarios, the capacity factor values are: 29.9%, 30.8%, 30.4%, 29.3%, and 35.6%. For the longest distance, 5D, the capacity factor values are: 28.9%, 29.9%, 29.4%, 27.6%, and 30.6%. The value of the capacity factor for an optimal layout; is achieved at 39.3%.

Suggested Citation

  • Bukurije Hoxha & Igor K. Shesho & Risto V. Filkoski, 2022. "Analysis of Wind Turbine Distances Using a Novel Techno-Spatial Approach in Complex Wind Farm Terrains," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13688-:d:950079
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    References listed on IDEAS

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    2. Hassna Salime & Badre Bossoufi & Youness El Mourabit & Saad Motahhir, 2023. "Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation," Sustainability, MDPI, vol. 15(2), pages 1-20, January.

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