本研究首先以風數據評估風機安裝評選合適之風機，以台南七股鹽山觀光區之「中小型風力機系統測試實驗室」為預設地點，並在此地設置一座測量高度為13m的小型測風塔進行風速觀測。接著以韋伯分佈擬合風速分佈後計算風機發電量，再以成本會計中資本預算決策之還本期間與內部報酬率來評選合適的小型風機進行分析與討論。而所有被評選之小型風機必須為經過國際認證機構驗證，符合IEC 61400-2之規範。
另一部份分析風機實際發電量狀況，透過資料採集與監控系統(SCADA)取得桃園大潭地區設置的兩隻風機本身之數據，包含一分鐘平均葉片轉速、偏向角與發電數據。並同時利用LEOSPHEREWINDCUBE v2固定式光達系統至風機現場測量風數據。而光達以都卜勒波束掃瞄法(DBS)收集，依四個方向相繼地發出傾角28°的光束量測徑向風速，第五條垂直光束直接量測垂直風速，以每分鐘約15筆資料解析度計算量測高層一分鐘平均之風速與風向。接著驗證並分析紊流強度後篩除風速小於2m/s數據，將風機偏向角與風向做回歸分析做補償修正。再透過交互比對風向及修正後的轉向角資料，還原風機在不同入流角時之發電功率對葉片轉速的功率曲線。最後統計入流角對風機發電功率的影響後，結果顯示入流角大於15°即會明顯降低輸出功率，且程度並不小於風機受到尾流的影響。

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.

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