Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
Browse
3 results
Search Results
Master Thesis Wind Turbine Control Via Power Measurements in Complex Terrain(01. Izmir Institute of Technology, 2022) Bingöl, Ferhat; Bingöl, Ferhat; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThis 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 Short-Term Wind Speed and Power Forecasting: a Comprehensive Case Study for Three Operational Wind Farms(01. Izmir Institute of Technology, 2022) Bingöl, Ferhat; Bingöl, Ferhat; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyWind energy is gradually growing with the increasing energy demand. However, the rising wind power penetration into modern grids could seriously affect the safe operation of power systems and power quality due to the intermittence and randomness of wind characteristics. Several effective ways could be considered to mitigate these issues: a robust power grid, energy storage, and wind power forecasting. Optimal integration of wind energy into power systems calls for high-quality wind power predictions. This research focuses on the short-term forecast of wind speed and power generation. Firstly, wind speed forecasting is studied. A case study is performed to analyze the forecasting performance of five approaches: the multivariate Facebook Prophet, seasonal autoregressive integrated with moving average (SARIMA), SARIMA with exogenous variable (SARIMAX), gated recurrent units (GRU) and long short-term memory (LSTM). The performance indicators are applied to verify the effectiveness of models, which are R-square (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The predictions obtained by the LSTM model almost coincide with the real-time wind speed, which is also supported by the performance indicators, which indicate that the LSTM model outperforms the other methods for the real-time dataset of IZTECH meteorological mast. The second part of the study is to forecast the wind power generation using the LSTM model and the wind speed forecasts and wind speed power curve of wind turbines in the wind farms. The proposed model is validated using the real-time wind power generation data from the EPIAS Transparency Platform. Due to the unavailable meteorological dataset, an ERA5 dataset of the location is used to predict wind speed and power generation. Also, each wind farm's daily forecasts are obtained to investigate the results for Day-ahead Market. The results indicate that using the LSTM model with the ERA5 dataset could give better forecasts than wind farms’ own forecasts. Additionally, it is understood that if the SCADA data could be obtained, the forecasting performance might be increased.Master Thesis Atmospheric Effects on Short Term Wind Power Forecasting(01. Izmir Institute of Technology, 2021) Kalay, Yüksel; Bingöl, Ferhat; Bingöl, Ferhat; Bingöl, Ferhat; 01. Izmir Institute of Technology; 03.06. Department of Energy Systems Engineering; 03. Faculty of EngineeringWind power all over the world are being popularizing unlike decrease in conventional sources due to environmental issues. However, power acquired from wind is not stable during day and night, which means that intermittent due to nature of the source. Forecasting in wind power plant is very challenging compared to forecasting of production of conventional power plant. Although there are many robust and site-specific models in order to forecast wind power accurately, decrease of deviation in wind power forecasting by using statistical, physical and hybrid models is still open to new approaches. In this study, four different forecast models based numerical weather prediction (NWP) models for three different wind farms which have different atmospheric conditions are examined to improve wind farm-based power forecasting. For this purpose, wind power forecasting of the providers was categorized based on atmospheric effects, which are site temperature and turbulence. Results have been compared with real time power production from wind turbine supervisory control and data acquisition (SCADA) system. Afterwards, new method based on selecting best provider for specific condition was developed by considering atmospheric effects on power forecasting. It should be noted that the method is an engineering approach, not a new forecast model. In many cases, newly developed method has succeeded to outperform in comparison to results belonging to forecast providers. Hourly and daily wind power forecasting that have significant role in electricity market has been improved for selected wind farms by the help of an engineering approach used in this study. Same method is also implementable to another wind farm if required inputs exist.