Data analytics for Canadian commercial wind energy

加拿大风能研究所（WEICan）既是一个研究机构，也是一个风电场的所有者/运营商。与加拿大其他商业风力发电场一样，WEICan也有兴趣利用运营数据中的智能来帮助他们提高农场的盈利能力。一个灵活的工具，能够识别最重要的风电场运行变量进行监测和分析，将为WEICan作为风电场所有者提供重要价值。这对作为国家研究机构的WEICan来说也是至关重要的，因为它正在领导一项倡议，以标准化加拿大商业风电运营的基本运营数据收集。随后，该提案的直接目标是创建一个概念验证

The Wind Energy Institute of Canada (WEICan) is both a research institute and a wind farm owner/operator. Like other Canadian commercial wind farms, WEICan is interested in leveraging intelligence from their operations data to help them improve farm profitability. A flexible tool capable of identifying the most significant wind farm operations variables to monitor and analyze would provide significant value for WEICan as a wind farm owner. This is also of critical interest to WEICan in its capacity as a national research institute where it is leading an initiative to standardize essential operations data collection for Canadian commercial wind operations. Subsequently, the immediate objective of the proposal is to create a proof-of-concept software tool capable of better characterizing, and improving wind farm performance. Such a tool will further enable WEICan to execute their leadership role in data collection standardization for the Canadian/International wind energy sectors. It will also empower WEICan as a wind farm owner to reveal strategic opportunities to improve their economic advantage over their competition; through the ultimate development of near-real-time operational analytics. A relationally structured, object-capable, database will be developed to integrate incongruent data elements (production data, meteorology, maintenance logs and reports, inspection photos, structural monitoring instrumentation, etc.) into a central location. A GIS referenced graphical interface will be created to make queries more intuitive to construct. Outcomes from more comprehensive queries and analysis could begin to reveal causation for underperformance and component wear. Structural ageing may be able to be mapped when combined with probabilistic loading models. Finally, the actionable intelligence derived from the tool may be leveraged in potential control optimization scenarios, i.e., the detection and subsequent sector management of wake flows, etc. The tool will be developed with sample data from two different commercial wind farm operations. Test queries and analyses will be evaluated against previously validated "hand assembled" historical analyses.