Performance improvement of modern variable-velocity wind turbines technology based on the doubly-fed induction generator (DFIG)

In the latest decades, Green energies received significant support from the international community and became the key solution for the global environmental issues caused by the increasing consumption of fossil fuels. One of the cleanest sources of renewable energy that allow producing green energy is wind power which is developing with a fast rate and becoming mature and more economically competitive due to the advanced techniques for extraction. Today, the modern wind power systems technology is based on the variable-velocity Doubly Fed Induction Generators (DFIGs). To improve the operation, the production and the stability of the modern variable-velocity wind turbines technology, the current paper proposes an adaptive backstepping control structure for a wind power system based on the doubly-fed Induction generator (DFIG) in power grid-connected mode. This adaptive nonlinear control structure is proposed to cope with nonlinearity and effects of the parameter uncertainties and variations in the DFIG wind power system under study, whose main purpose is the power extract optimization from variable-velocity DFIG wind turbine while enhancing the stability of the whole system. Design of the adaptive nonlinear backstepping control system as rotational velocity and rotor current regulators have been implemented based on the wind power system dynamic model with unknown parameters. The system's unknown parameters are dynamically estimated through the parameters adaptive update laws formulated during the design process, and these estimation laws together with the control actions for the DFIG-wind turbine are derived in such a way that the stability of the whole system according to the Lyapunov stability theory is ensured. Numerical simulation tests were conducted to evaluate the robustness and effectiveness of the proposed adaptive control structure. The obtained results show clearly that the proposed method has advanced robustness to cope with the system parameter variations and uncertainties. (c) 2021 Elsevier Ltd. All rights reserved. Second International Conference on Aspects of Materials Science and Engineering (ICAMSE 2021).