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• W3097816690 endingPage "110434" @default.
• W3097816690 startingPage "110434" @default.
• W3097816690 abstract "Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism." @default.
• W3097816690 created "2020-11-09" @default.
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• W3097816690 date "2021-05-01" @default.
• W3097816690 modified "2023-10-18" @default.
• W3097816690 title "Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression" @default.
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• W3097816690 doi "https://doi.org/10.1016/j.envres.2020.110434" @default.
• W3097816690 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33166537" @default.
• W3097816690 hasPublicationYear "2021" @default.
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