Posters

Abstract

It is undisputable that air density has an influence on wind turbine performance, and this needs to be accounted for when performing analysis following the industry best practices. Unfortunately, in most SCADA analysis performed today, this simple normalisation is either partially or completely excluded. This is typically due to the absence in the studied dataset of one or more of the appropriate signals, namely pressure, temperature and humidity measurements (PTH). Interconnection of databases (DB) using Application Programming Interface (API) is now easier than it ever was, and a multitude of web services have emerged proposing an automated access to various datasets including, of course, historic meteorological measurements. The worldwide coverage, ease-of-use, availability and low-cost of these data providers now enables their permanent integration in any data related process, especially when considering large-scale recurring fleet analysis. In this study, we explored the validity of using these weather APIs in the context of air density normalisation of wind turbine SCADA data. When available, we compared the resulting correction with the one obtained using wind farm PTH sensor. Given the excellent results of this pragmatic approach, it was deemed suitable for immediate deployment and used for regular, automated analysis of the performance of over 3000 wind turbines. In this presentation, we will also discuss the challenges encountered when deploying methodology on such scale. The adoption of this method enables asset managers, often bound to SCADA datasets, to refer to more accurate power curves which comply more closely with industry best practices.