Prediction of operating parameters and output power of ducted wind turbine using artificial neural networks

Abstract

The performance of a ducted wind turbine was simulated in this study utilizing an artificial neural network under various duct operating conditions. Ducted wind turbines have been identified as one of the cleanest and most effective future renewable energy possibilities. Different methods have been used throughout time to exploit the flow stream power successfully. However, the Ducted Wind Turbine System is one method that is still in its early phases. The operation of the ducted wind turbine is analyzed in this research under different wind conditions. The ANN algorithm is recommended for generating turbine power at various wind speeds. The proposed neural network model is shown to perform better for estimating turbine power curves. The ANN models used the free-stream wind speed, wind speed in the throat section, turbulence intensity, and wind power in the intake part of the duct as input datasets and the output power of wind in the throat section as output datasets. At the same time, a three-layer backpropagation training method from the Multilayer Perceptron algorithm approach, a Levenberg–Marquardt, was selected as the best ANN. It was evaluated by comparing to a Radial-based functions approach with a single hidden layer. The optimum number of neurons in the MLP method’s first and second hidden layers, as well as the RBF method’s single hidden layer, has been determined to be 55 and 70, respectively. At 77 and 70 epochs, the best MSE evaluation efficiency of MLP and RBF networks was estimated to be 0. 000103 and 0. 000353, respectively.