Mohammad Hassan Khoshnevis - MSc, School of Aerospace Engineering, College of interdisciplinary sciences and technologies, University of Tehran, Tehran,Iran, Postal Code:۱۴۳۹۹۵۷۱۳۱
Shahrokh Shams - Associate Professor, School of Aerospace Engineering, College of interdisciplinary sciences andtechnologies, University of Tehran, Tehran, Iran, Postal Code:۱۴۳۹۹۵۷۱۳۱,

This research investigates the power calculation of a H-type cable wind turbine is based on an artificial neural network. The proposed hypothesis includes the structure of the wind turbine as a cable under tension, and its type as a vertical axis and H-type cable. The type of analysis is not linear due to the shape and load of the structure, as well as the materials used, and the Double Multiple Stream Tube model is employed to calculate the power generated by a wind turbine. Here, The Artificial Neural Network or ANN method is employed to minimize computation time and minimize design and analysis expenses, as well as to achieve a method for analyzing aerospace structures with the ability to generalize. The innovations carried out in this research include the derivation of generated power equations for the cable structure of a H-type cable wind turbine as well as the prediction of amount of generated power using ANN algorithm without the need to perform modeling and spending time running analytical software in the least time with the highest level of accuracy and minimum error. The determination of generated power in this investigation led to the creation of a design code for a H-type cable vertical axis wind turbine. Eventually, the analysis speed comparison between the two methods DMST and ANN shows that the ANN method has a higher analysis speed than the other one.

Wind Turbine, H-type, Cable, Power, ANN.