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Gantasala, Sudhakar; Luneno, Jean-Claude; Aidanpaa, Jan-Olov (2016)
Languages: English
Types: Article

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics, Astrophysics::Earth and Planetary Astrophysics, Physics::Geophysics, Physics::Fluid Dynamics
Wind turbines installed in the cold climate regions accumulate ice on the blades affecting their aeroelastic behavior and turbine power output. It is essential to detect icing in the early stages to start the deicing systems so that the losses due to icing can be minimized. The increase in mass distribution of the blade due to icing reduces its natural frequencies and how much these frequencies reduce depends on the amount of ice mass and their location on the blade. Ice detection systems like BLADEControl (Bosch Rexroth) and fos4blade IceDetection (fos4X) systems detect ice based on the deviations in blade natural frequencies, but cannot identify the location and amount of ice mass. In this work, the authors propose a method to detect average ice mass accumulated along three zones defined along the blade based on its natural frequencies using Artificial Neural Networks (ANN). Different ice masses are added on a wind turbine blade and their natural frequencies are simulated using a finite element model of the blade vibrations. ANN is trained with the natural frequencies of the iced blade as inputs and corresponding ice mass distributions used in the three zones as outputs. ANN approximates the non-linear function between inputs and outputs in the training process. After training with a large data set of possible ice mass distributions, ANN model can be used to predict ice mass distributions in the three zones for any set of natural frequencies (input to ANN) of the iced blade. NREL 5 MW wind turbine blade is considered in this study to demonstrate the proposed method. Various cases of ice mass distributions are tested by the trained ANN model and the predicted ice mass distributions are compared against actual ice mass distribution values. ANN model is able to predict ice mass distributions exactly if they are similar to the ice mass distributions used in the training data, otherwise the ice masses are predicted with an error. Overall, the proposed method is able to approximately detect average ice mass accumulated along the blade which is not possible before.
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