Data analytics for Canadian commercial wind energy

加拿大风能研究所（WEICan）既是一个研究机构，也是一个风电场所有者/运营商。与其他加拿大商业风电场一样，WEICan有兴趣利用其运营数据中的情报来帮助他们提高风电场的盈利能力。一个灵活的工具，能够识别最重要的风电场运营变量进行监测和分析，将提供重要的价值，为威康作为一个风电场业主。这也是至关重要的兴趣WEICan作为一个国家研究机构的能力，它是领导一项倡议，标准化的基本操作数据收集加拿大商业风力发电业务。因此，该提案的直接目标是创建一个概念验证 软件工具能够更好地表征，并提高风电场的性能。该工具将进一步使WEICan能够在加拿大/国际风能部门的数据收集标准化方面发挥领导作用。它还将使WEIC作为风电场所有者能够通过最终开发近实时运营分析来揭示战略机遇，以提高其在竞争中的经济优势。将开发一个具有相关结构的、对象能力的数据库，以整合不一致的数据元素（生产数据、气象、维护日志和报告、检查照片、结构监测仪器等）。到一个中心位置。一个GIS参考 将创建图形界面，使查询更直观地构建。更全面的查询和分析结果可能会开始揭示性能不佳和部件磨损的原因。当与概率荷载模型相结合时，结构老化可能能够被映射。最后，从工具导出的可操作智能可以在潜在的控制优化场景中被利用，即，尾流的检测和随后的扇区管理等。该工具将使用来自两个不同商业风电场运营的样本数据进行开发。将根据先前确认的“手动组装”历史分析评价测试查询和分析。

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.