This paper presents a controller performance that is develop employing an adaptive B-spline neural network algorithm for adjusting the rotor speed of the permanent magnet synchronous motor. It includes a comparative analysis with three control strategies: conventional proportional integral, sliding mode and fuzzy logic. Also, gives a systematic way to determine the optimal control gains and improve the tracking error performance. A methodology for the adaptive controller and its training procedure are explained. The efficacy of the proposed method is analyzed using time simulations where the motor is subjected to disturbances and reference changes. The proposed control technique exhibits the best performance because it can adapt to every condition, demanding low computational effort for an on-line operation and considering the system nonlinearities.

The wind energy boom in the world began in 1980's. This paper presents the modeling and control of a Wind Energy Conversion Systems (WECS) based Permanent Magnet Synchronous Generator (PMSG). The control scheme uses a Bspline artificial neural network for tuning controllers when the system is subjected to disturbances. The currents from VSI's are controlled in a synchronous orthogonal dq frame using an adaptive PI control. The B-spline neural network must be able to enhance the system performance and the online parameters updated can be possible. This paper proposes the use a linear control PI to regulate the DC link voltage. Simulation results show the feasibility and robustness of the proposed control schemes for PMSG based wind turbines. Comprehensive models of wind speed, wind turbine, PMSG and power electronic converters along with their control schemes are implemented in MATLAB/SIMULINK environment.

No abstract

No abstract