Come meet the poster presenters to ask them questions and discuss their work
Check the programme for our poster viewing moments. For more details on each poster, click on the poster titles to read the abstract.
PO004: Progress in Aerodynamic Imbalance Detection : Pitch Health Indicator using SCADA data
Nicolas Quiévy, Wind Technology Manager, ENGIE
Rotor blades are pitching above nominal wind speed in order to limit the loads on the turbine. This change in the rotor geometry decreasing the aerodynamic lift is called active pitch regulation. Below nominal power, there is no pitch control. As much as possible power is extracted from the wind by optimizing the rotor speed (torque control). However a design angle of attack has to be respected, which supposes a correct reference pitch angle. A misalignment of the static pitch can thus result in aerodynamic imbalance with increased fatigue loading and/or reduced wind turbine performance (typically in the range of 1%). Pitch misalignment can be absolute (one blade compared to its design position) or relative (on blade compared to another one). DNV provides acceptable values for absolute (+/- 0.3°) and relative (0.6°) misalignment. Pitch misalignment can be detected in the field using measurement devices. However it is time consuming, weather dependent and can only be applied to one turbine at a time with the risk of inspecting a healthy turbine. Therefore a method was developed by scrutinizing SCADA data with the objective to identify turbines with rotor imbalance. With 1Hz or higher frequency, i.e. below 1P frequency, data would be ideal to carry out such an analysis and make it more powerful (e.g. identifying the blade(s) that is (are) misaligned). However such data are not always accessible. 10 minutes SCADA data (specific tags) were investigated instead and showed to be a relevant input for defining a Pitch Health Indicator (PHI). The developed algorithm was applied to part of ENGIE fleet to highlight turbines suffering from rotor imbalance and follow corrective actions. Good correlation was obtained with field results from measurement campaigns. The PHI detects the turbines suffering from pitch misalignment in a qualitative way, allowing to better target pitch misalignment angle measurements and correction in the field afterwards.