Wind Energy Conversion System Control Inclusive Characterization with Soft Computing Techniques

  • Vinod A., D. Krishna G., Amarnath


 Here, for control of wind energy system and altering the speed drive design, the strength and energy of those systems is traditionally spread. Now-a-days, various better electric perceptible tracking schemes are refined and established. The past MPPT disciplines systems be authorized mainly into four categories i.e. hill climbing searching, particle swarm optimization searching, tip speed ratio and feed-back control. Though, aforesaid vast measure of variations are outlined up the last fifty years so that it’s emerge as inconvenient to see that technique, novel or fresh planned, its better trendy for a accustomed wind system.  Here combination of both gravitational search algorithms with artificial neural network is proposed. Present the gravitational search algorithm procure the input parameters from the rectifier outputs.   The rectifier dc current, dc voltage and time. From the input parameters it maximizes the minimized error power of the rectifier outputs such as rectifier dc training dataset depending on the maximum power point tracking (MPPT) condition. The training dataset has been utilized for the artificial neural network. During the testing time it provides the reference dc-side voltage of the rectifier which has been converted into control pulses switch. Finally the proposed method is implemented in the software (matlab) platform and the effectiveness is analyzed by using the comparison of hill climb searching and particle swarm optimization technique where hill climbing searching and particle optimization technique has the disadvantage is the possibility of the failure to quick changes in wind speed.

How to Cite
Vinod A., D. Krishna G., Amarnath. (2020). Wind Energy Conversion System Control Inclusive Characterization with Soft Computing Techniques . International Journal of Advanced Science and Technology, 29(3), 14850 -. Retrieved from