台灣中西部離岸風能潛勢評估之研究

Offshore Wind Potential Assessment of West Central Taiwan

徐文科(Wen-Ko Hsu)

指導教授 ： 葉仲基

國立臺灣大學/生物資源暨農學院/生物機電工程學系/博士(2022年)

https://doi.org/10.6342/NTU202200273

摘要

本研究探討台灣中西部外海離岸風場之風能潛勢，藉由鄰近陸域風力機運轉資訊(Wind Turbine Generator SCADA Data)及光達(Light Detection and Ranging, LiDAR)量測之資料，搭配量測推估法(Measure-Correlated-Predict, MCP)推估出離岸風場區域之風力分佈，將可據以進行風能潛勢分析。
首先統計分析離岸海氣象觀測塔(Met Mast)之觀測資訊，來了解台灣中西部外海近岸地區離岸風能密度大小與分佈情形，後續透過常見風力發電機性能曲線(Power Curve)來進行產能評估，包含年發電量 (Annual Energy Production)與容量因數(Capacity Factor)等，並以本區域風能分佈情形來評估潛在機組的產能評估與其適應性。
其次以鄰近海氣象觀測塔之陸域風力發電機運轉資訊，配合光達 (LiDAR)量測比對，以量測推估(MCP)法取得風能分佈情形，光達可同時量測特定高層風速，進而取得所需高層風能分佈，並藉由比對海氣象觀測塔資料風力發電機產能評估，來確認推估方式取得風能分佈之可行性。
本研究所提以風力發電機運轉資訊配合光達進行量測推估之風能調查方法實屬可行，可有效縮短風能分佈取得所需時間及降低成本，對後續離岸風能開發將有所助益。

關鍵字

風力發電機運轉資訊 ； 光達 ； 量測推估法 ； 海氣象觀測塔 ； 性能曲線 ； 年發電量 ； 容量因數

並列摘要

This study investigates the offshore wind potential assessment of the west central Taiwan offshore site. Combining onshore wind turbines generator SCADA data with Light Detection and Ranging (LiDAR) and using the Measure-Correlated-Predicts (MCP) method, we were able to reasonably evaluate the wind resource of the specific offshore site. This will allow us to obtain the wind resource distributions for estimating power production.
The initial step utilizes met mast measured data to determine the wind distribution and power density of the west central Taiwan offshore site. Power curves of the specific wind turbines were used to obtain the power production evaluation, including annual energy production (AEP) and capacity factor (CF). Accordingly, the suitability of wind turbines can be assessed based on the achieved wind resource distributions.
The second step involves the measurement data onshore LiDAR along with the SCADA data from the onshore wind turbines to evaluate wind distributions base on MCP method. Using different level data from LiDAR, we can approach the specific wind resource distributions. The accuracy and viability of these evaluations were verified by the offshore met mast measured data and the wind power productions.
The study proposes a process to approach wind resource distribution based on LiDAR and SCADA measured data from the wind turbines, using the MCP method. This would benefit in time saving and financial cost for further offshore wind farm development.

並列關鍵字

Wind Turbine Generator SCADA Data ； Light Detection and Ranging (LiDAR) ； Measure-Correlated-Predict (MCP) ； Met Mast ； Power Curve ； Annual Energy Production ； Capacity Factor

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