2011 - Day Zero - Renewables User Group

Strategies for Running Advanced Analytics Using MATLAB and PI System

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Most wind energy consultants claim that they can easily increase a wind farm’s performance by 1% with the help of specialized tools and techniques (analytical tools, condition monitoring, O&M plan, blade cleaning/balancing, etc.). Unfortunately, none of them have ever properly demonstrated with statistics the energy increase they claim to be able to achieve, which makes their reliability questionable. For this reason, an Artificial Neural Network (ANN) model have been developed and show better precision than the well renowned IEC61400-12-1 and IEC61400-12-2 standards. This increase in performance can be explained mainly by the fact that a limited number of inputs (e.g. wind speed, temperature, turbulence intensity) are normally used with IEC models. The newly developed models are able to incorporate more inputs than ever before (e.g. wind speed, temperature, wind veer, wind shear, and other new metrics related to high frequency data acquisition). Three wind farms with over 200 wind turbines have been equipped with the PI System that enables the acquisition of more than 100 000 variables per second. The sampling frequency of 1 hertz (or higher) (instead of 10 minutes-averaged data), the quantity of data (>2 years) and the number of variables (more than 700 variables per wind turbines), make this database UNIQUE for the wind industry and have enabled the development of novel methodologies for power performance evaluation and others possibilities related wind farm condition monitoring.

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Industry

Power & Utilities

Company

École de Technologie Supérieure

Speaker

Francis Pelletier

PhD Candidate

Company

École de Technologie Supérieure

Speaker

Christian Masson

Phd Director

Company

École de Technologie Supérieure

Speaker

Antoine Tahan

Co-Director