Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Wind Turbine Control Via Power Measurements in Complex Terrain(01. Izmir Institute of Technology, 2022) Dirik, Deniz Gökhan; Bingöl, FerhatThis work presents an approach to the assessment of wind farm yaw control to utilize wake steering in complex terrain based on power measurements. Aerodynamic interactions between closely spaced wind turbines reduce the power output significantly. The standard wind turbine control strategy currently focuses on optimizing the wind turbines individually. However, there is growing evidence that these wake losses can be improved by optimizing for aerodynamic interactions between the turbines. In a case study, an assessment of wake steering gain and optimum yaw offset angles for each wind turbine are simulated for an operational wind farm. Wake losses are simulated for the wind farm and are validated using historical power measurements. Data analysis procedures for implementing operational wind farm data for the wake steering approach are described. Optimum yaw offset angles are calculated in simulations using operational data. A lookup table is generated for the optimum yaw angles required for each wind direction and speed bin. Using 5-year-long operational data, an average of 0.48% wake losses are calculated for the site. FLORIS simulations suggest 9.6% possible power improvement in wake losses using the optimum yaw offset angles. Using SCADA measurements for potential wake steering assessment allows rapid assessment and implementation without requiring expensive and year-long LIDAR or meteorological mast tower measurements.Master Thesis Coupled Wake and Blockage Modelling for a Wind Farm(Izmir Institute of Technology, 2022) Çam, Janset Betül; Bingöl, FerhatOne of the significant reasons for the power loss in wind farms is the wake effect. Therefore, the wake effect is crucial for designing a wind farm. However, only wake modeling is not sufficient to explain power losses. Wake is the turbulent, complex, and relatively weak flow behind the wind turbine. The wake effect is not required for the front row turbines in wind farms, and the wake model cannot be applied. It is assumed that the wind farm directly encounters the free stream wind speed. However, the blockage effect, also known as the induction zone effect, is observed at the front of the wind turbines. Due to this effect, the wind farm encounters a lower wind speed than the free-stream wind speed. This situation reduces the accuracy of the Annual Energy Production (AEP) calculation in wind farms. The motivation of this study is to obtain an improved coupled wake and blockage model that converges to the accurate SCADA data of a wind farm more than the wakeonly or blockage-only models. This study applies seven wake and six blockage models to the wind farm. The similarities and differences between the coupled models and the wind farm SCADA data and their reasons are discussed.