Marti-Puig, P.; Hernández, J.Á.; Solé-Casals, J.; Serra-Serra, M. Enhancing Reliability in Wind Turbine Power Curve Estimation. Appl. Sci.2024, 14, 2479.
Marti-Puig, P.; Hernández, J.Á.; Solé-Casals, J.; Serra-Serra, M. Enhancing Reliability in Wind Turbine Power Curve Estimation. Appl. Sci. 2024, 14, 2479.
Marti-Puig, P.; Hernández, J.Á.; Solé-Casals, J.; Serra-Serra, M. Enhancing Reliability in Wind Turbine Power Curve Estimation. Appl. Sci.2024, 14, 2479.
Marti-Puig, P.; Hernández, J.Á.; Solé-Casals, J.; Serra-Serra, M. Enhancing Reliability in Wind Turbine Power Curve Estimation. Appl. Sci. 2024, 14, 2479.
Abstract
Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.
Keywords
Wind Power Curve Modeling; Artificial neural networks (ANN); Wind Turbine (WT); SCADA Data.; Industrial AI
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
