Gene

Quantum mechanics

Control (management)

Voltage

Engineering

Statistics

Robust control

Control system

Robustness (evolution)

Biochemistry

Computer science

Maximum power point tracking

Wind speed

Induction generator

Physics

Nonlinear system

Sliding mode control

Mathematics

Inverter

Artificial intelligence

Parametric statistics

Wind power

Chemistry

Meteorology

Control theory (sociology)

Artificial neural network

Machine learning

Integral sliding mode

Electrical engineering

Total harmonic distortion

Energy Reports

Handling the internal parametric variations and the nonlinearities of the high rated wind energy conversion system (WECS) is remained among the main challenges to maximize the produced energy, ameliorate its quality and ensure its efficient integration on the grid. In this context, a robust integral sliding mode control (ISMC) with Lyapunov function is proposed to control the active and reactive powers of a doubly fed induction generator (DFIG) based wind turbine, and to assure high dynamic performances according to the wind speed variation. To operate around an optimal rotational speed, a robust MPPT algorithm with mechanical speed control based on artificial Neural Network Controller (ANNC) is presented in order to extract the maximum power. Thereafter, the robust integral SMC are replaced by Field Oriented Control (FOC_PI) for comparative purposes. The objective is to prove the best performances of the system obtained by the proposed control method in terms of the dynamic response, total harmonic distortion THD (%) of the injected current into the grid, the reference tracking ability, Overshoot (%), precision and robustness. The effectiveness and robustness of each control techniques has been implemented and tested under MATLAB/Simulink environment by using a 1.5 MW wind system model.

Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile