Posters

Abstract

In this work, a comprehensive framework is proposed for diagnosing static errors affecting wind turbine performance, based on SCADA data analysis. The three types of considered static errors are: * Static yaw error; * Absolute or relative blade pitch misalignment; * Anemometer bias. It is desirable to exploit as much as possible the SCADA-collected data for diagnosing such types of static errors. Yet, given the mediation of the control system, the SCADA data might even be misleading and, for example, might indicate a correct alignment of the rotor to the wind flow, while it is not correct. In the literature, it has not been systematically elaborated what manifestations (observable through SCADA data) can be associated to the various types of static errors. Hence, the objective of the work is filling such research gap by identifying phenomena observable through SCADA data which can be related to such static errors. Acknowledged that some manifestations can be similar for the various considered errors, a work flow is set up for distinguishing between them. The selected phenomena are: * Under-performance, which is associated to all the errors; * Increased vibrations; * Shift in the nacelle anemometer measurements. Particular attention is devoted to the impact of the various errors on the nacelle anemometer measurements, as this aspect has been overlooked in the literature. Real-world test cases, namely wind farms operated by the ENGIE Italia company, are discussed. A combination of single wind turbine and fleet-wide methods is proposed. The latter involve dividing the fleet in potentially anomalous and normal wind turbines and modelling quantities of interest (like the power) as a function of the corresponding quantities of the healthy wind turbines. The collected case studies indicate that the proposed method is effective and allows distinguishing between the various types of errors related to the anemometer, the yaw and the pitch systems.