Comparative anomaly detection for floating offshore wind turbines using in-situ data

Abstract. This study introduces a comparison between a kurtosis-analysis and a deep learning-based approaches for detecting anomalies of a floating offshore wind turbine. The study uses in-situ measurements from a 2.3 MW floating offshore wind turbine named Zefyros and deployed approximately 11 kilometers off the coast of Norway. The first method employs the statistical metric of kurtosis to detect anomalies within a signal by identifying variations in the signal distribution. The second method employs a deep-learning procedure based on an auto-encoder approach, which transforms inputs into a reduced-dimensional latent space and then uses the encoded information to produce outputs identical to the inputs. One month of SCADA and high frequency measurements obtained thanks to S-Morpho sensors were used in the study. Due to limitations in the accessible SCADA information, the anomaly scenario was simplified to detecting whether the turbine rotor was rotating or not. Both tested methodologies can accurately detect anomalies, with the ground truth based on rotor rotations per minute (rpm) measurements. The auto-encoder method shows promising results, delivering more accurate outcomes than the kurtosis analysis on this in-situ measurement dataset. This study is a first step toward a more general use of auto-encoders for wind turbine anomaly detection. The latent space build by the auto-encoder can be leveraged to detect other types of anomalies, with a few labeled data.