||Assessment of Wind Turbine Installation and Evaluation on Wind Power Generation Using On-Site Wind Data
||Department of Mechanical Engineering
This research initially assesses wind turbine installation to select a suitable wind turbine. The default location is at the “Medium and Small Wind Turbine Testing Laboratory” which is next to the Salt Mountain tourist area in Cigu, Tainan. In order to measure wind data, a 13m height small meteorological mast was erected to install cup anemometers and wind vanes. Then, statistical wind data was fitted using a Weibull distribution to calculate the energy production of the turbines, and then an analysis and discussion of how to choose a suitable turbine in view of internal rate of return (IRR) and payback period were conducted. Nevertheless, an economical turbine is relevant to initial capital cost. All of the selected turbines were required to be certified by international certification bodies and to conform to IEC 61400.
Another part of the research was an evaluation of wind power generation. Data from two large wind turbines was acquired from the Supervisory Control and Data acquisition (SCADA) system, deployed in the Taoyuan Tatan District. The SCADA data include the blade angular velocity, turbine yaw angle and power production recorded as a 1 minute average value. At the same time, the LEOSPHERE WINDCUBE v2 LiDAR system was used to measure wind speed and direction in the field. The approach was based on the Doppler Beam Swinging method to collect the light-of-sight wind speed. In this method, four beams are successively sent along a 28° cone angle in four cardinal directions, followed by a fifth vertical beam sent to measure vertical velocity directly. The resolution of the 1 minute average wind data from LiDAR is 15 records per minute. Wind speed data below 2m/s was removed after verification and an
analysis of turbulence intensity, and then a regression analysis was conducted to compensate for the yaw angle. By making interactive comparisons between wind direction and the compensated yaw angle, the power curve of the different inflow angle to blade rotations was obtained. Finally, the statistics show that the turbine averaged power output will result in a considerable decline at an inflow angle greater than 15°, and the influence is not less than the wake effect.
List of Tables III
List of Figures V
1. Introduction 1
2. Literature Review 8
2.1 Weibull distribution 8
2.2 Wind turbine characteristics 11
2.3 Wind turbine classes 13
2.4 Wind measurement 17
2.5 Effects of Yaw Error on wind turbine performance 21
3. Experimental Apparatus and Research Methods 26
3.1 Assessment of wind turbine installation 26
3.1.1 Site 27
3.1.2 Meteorological mast system 28
3.1.3 Identification of small wind turbine 36
3.2 Evaluation on wind power generation 39
3.2.1 Site of the wind farm 39
3.2.2 Fixed LiDAR 42
3.2.3 Assessment of obstacles 46
3.3 The calculation methods for wind data 48
3.3.1 Wind distribution 48
3.3.2 Discrete energy spectrum 48
3.3.3 Power curve 49
3.3.4 Annual energy production 50
3.3.5 Capacity factor 51
3.3.6 Capital budgeting 51
4. Assessment of wind turbine using wind data 54
4.1 Data processing 54
4.2 Assessment of wind data 61
4.3 Wind turbine performance 65
4.4 Capital-budgeting decisions 69
5. Evaluation on power generation using wind data 71
5.1 Assessment of wind data 71
5.2 Wind direction at layers 76
5.3 Uncertainty of the nacelle anemometer 78
5.4 Inflow angle effect 88
6. Conclusions 96
7. References 98
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