Abstract
The estimation of variables that are normally not measured or are unmeasurable could improve control and condition monitoring of wind turbines. A cost-effective estimation method that exploits machine learning is introduced in this paper. The proposed method allows a potentially expensive sensor, for example, a LiDAR sensor, to be shared between multiple turbines in a cluster. One turbine in a cluster is equipped with a sensor and the remaining turbines are equipped with a nonlinear estimator that acts as a sensor, which significantly reduces the cost of sensors. The turbine with a sensor is used to train the estimator, which is based on an artificial neural network. The proposed method could be used to train the estimator to estimate various different variables; however, this study focuses on wind speed and aerodynamic torque. A new controller is also introduced that uses aerodynamic torque estimated by the neural network-based estimator and is compared with the original controller, which uses aerodynamic torque estimated by a conventional aerodynamic torque estimator, demonstrating improved results.
Highlights
Operation and maintenance (O&M) costs account for a significant proportion of the total annual costs of a wind turbine
A single turbine in a cluster of several wind turbines is equipped with a sensor, and the remaining turbines are equipped with an neural network (NN) based estimator, which would significantly reduce the associated costs
The turbine equipped with the sensor is used to train the NN-based estimator, which essentially replaces potentially expensive sensors
Summary
Operation and maintenance (O&M) costs account for a significant proportion of the total annual costs of a wind turbine. Wind speed estimated by the proposed NN-based estimator could help to improve wind turbine control; that is not discussed in this paper. The NN-based method proposed in this paper is novel, as no other existing work realises wind speed estimation at a wind farm level taking into account the associated costs. The estimation method proposed here allows the use of wind speed and other useful variables that are normally not measured as part of the controller design. The primary contributions of this paper are the development of NN-based estimators (focusing on the estimation of wind speed and aerodynamic torque here) and the improvement of the wind turbine controller realised by incorporating the improved estimation of aerodynamic torque in the original controller design.
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