Maximum Power Extraction from PMSG Wind Turbine Based On Neural Network Predictive Controller

  • Faizan Khurshid Shah, Sachin Kumar, Rohit Kumar

Abstract

 In this paper, robust control is considered to extract optimum wind energy power from wind turbines and to minimize fluctuations in output parameters by regulating the pitch angle. Wind turbines drive Permanent Magnetic Synchronous Generator (PMSG), which is directly integrated by autonomous controllers. However, as wind power contributes to the distribution grid, the significance of a constant power wind turbine output is increasing. PMSG also has a Variable Pitch System (VPS) wind turbine with systems to monitor the turbine axis orientation angles towards the skyline and the wind. The modelling and MATLAB simulation of the PMSG VPS Neural Network Prediction Controllers (NNPC) which is used to forecast plant potential performance is illustrated. Its architecture is simulated by simulation of wind speeds at 11.5-14 m / s on a linear baseline controller. PMSG based wind turbine VPS was simulated to test the planned NNPC pitch angle outputs, resulting in continuous performance.

Published
2020-06-01
How to Cite
Faizan Khurshid Shah, Sachin Kumar, Rohit Kumar. (2020). Maximum Power Extraction from PMSG Wind Turbine Based On Neural Network Predictive Controller. International Journal of Advanced Science and Technology, 29(7s), 5692-5701. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/26455