A Novel Ellipsoidal Semisupervised Extreme Learning Machine Algorithm and Its Application in Wind Turbine Blade Icing Fault Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3205920
Abstract: The conventional semisupervised extreme learning machine (SS-ELM) algorithm can provide a solution to the lack of labeled samples in wind turbine blade icing fault detection, but its performance is limited by the irrationality of the… read more here.
Keywords: blade icing; wind turbine; algorithm; elm ... See more keywords
FedBIP: A Federated Learning-Based Model for Wind Turbine Blade Icing Prediction
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2023.3273675
Abstract: Prediction of icing on wind turbine blades is crucial, particularly in high-latitude areas where ice accumulation is a frequent occurrence. Traditional centralized data-driven approaches for predicting blade icing have demonstrated promising performance, but they require… read more here.
Keywords: blade icing; wind turbine; prediction; model ... See more keywords
Blade icing detection of wind turbine based on multi-feature and multi-classifier fusion
Sign Up to like & getrecommendations! Published in 2022 at "Wind Engineering"
DOI: 10.1177/0309524x221075590
Abstract: Wind farms are usually located in high altitude areas with a high probability of ice occurrence. Blade icing has the potential to result in unexpected mechanical failures and downtimes. In order to avoid these problems,… read more here.
Keywords: fusion; blade icing; multi classifier; wind ... See more keywords