Many developing nations, such as India have embarked upon wind energy programs for areas experiencing high average wind speeds throughout the year. One of the states in India that is actively pursuing wind power generation programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind farm concentration is high. Wind energy engineers are interested in studies that aim at assessing the output of wind farms, for which, artificial intelligence techniques can be usefully adapted. The present paper attempts to apply this concept for assessment of the wind energy output of wind farms in Muppandal, Tamil Nadu (India). Field data are collected from seven wind farms at this site over a period of 3 years from April 2002 to March 2005 and used for the analysis and prediction of power generation from wind farms. The model has been developed with the help of neural network methodology. It involves three input variables—wind speed, relative humidity and generation hours and one output variable-energy output of wind farms. The modeling is done using MATLAB toolbox. The model accuracy is evaluated by comparing the simulated results with the actual measured values at the wind farms and is found to be in good agreement.
A method for assessment of wind–hydrogen ( energy systems is presented. The method includes chronological simulations and economic calculations, enabling optimised component sizing and calculation of cost. System components include a wind turbine, electrolyser, compressor, storage tank and power converter. A case study on a Norwegian island is presented. The commuting ferry is modelled as a ferry, representing the demand. The evaluation includes a grid-connected system and an isolated system with a backup power generator. Simulation results show that much larger components are needed for the isolated system. cost amounted to and for the grid-connected and isolated system, respectively. Sensitivity analyses show that a marginal decrease in wind turbine and electrolyser cost will reduce the cost substantially. Rate of return is also important due to high investment costs. The grid-connected system is by far the most economical, but the system involves frequent grid interaction.