A data-driven approach is an alternative to extract general models for wind energy applications. A spatial sensitivity analysis is achieved using a probabilistic model to quantitatively identify the variability in performance due to individual parameters and visualize spatial distributions. Proper orthogonal decomposition results are combined with linear discriminant analysis under the clustering framework to present low-dimensional classifiers. Using the decomposition enables the system to be far away from ill-conditioned states. The optimal sensor locations are explicitly distributed in the transition region, where the velocity and Reynolds stresses relax toward a wake recovered state. With the optimal sensors, the cluster assignment and flow dynamics are obtained. There is an advantage in including more features in the reconstruction process to capture the slow and fast dynamics. Assessing the differences in the wake response and establishing the importance of spatial sensitivities are provided here for seeking accurate models. The bidirectional neural network is used to predict the fluctuating velocity of the considered sensors. The result of long–short term memory shows correlations of 92% between the real and predicted fluctuating velocities.
Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes
Note: This paper is part of the special issue on Advances in Wind Plant Controls: Strategies, Implementation, and Validation.
Naseem Ali, Marc Calaf, Raúl Bayoán Cal; Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes. J. Renewable Sustainable Energy 1 March 2021; 13 (2): 023307. https://doi.org/10.1063/5.0036281
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