This work develops a real‐time implementation on the DSPACE environment of an adaptive non‐linear control strategy for a wind energy conversion system (WECS) based on the doubly‐fed induction generator (DFIG). The DSPACE‐DS1104 board is directly associated with an experimental bench of a wind power system. The non‐linear backstepping controller has been realised to control the active and reactive powers of the DFIG connected directly to the electricity grid via two converters (grid side and machine side). First, a full review of the WECS is discussed. Subsequently, a detailed description of the backstepping control laws based on the Lyapunov stability technique is reported. Consequently, a simulation on the Matlab & Simulink environment was carried out to test the proposed control model in terms of performance and robustness. The second part of this work was devoted to the experimental validation of the adaptive backstepping control algorithm on a test bench to prove its efficiency. The results obtained show a perfect correlation between simulations and experiments (in static and dynamic regime) even for fluctuating wind speed.

<p>In the recent years, the development and the exploitation of renewable energy knew a great evolution. Among these energy resources, the wind power represents an important potential for that the wind system has been the subject of several researches. The purpose of this study is to improve the power extracted from wind energy, taking into consideration the variation of wind speed which causes a problem in energy production. For this purpose, we have controlled the powers whether it is active or reactive delivered by the generator. This paper, presents essentially the modeling and control of doubly- fed induction generator (DFIG), which is connected to a variable speed wind turbine. Firstly, the model of the wind power system with the maximum power point tracking (MPPT) strategy is shown. Then, the modeling of doubly- fed induction generator (DFIG) and its power control is presented. Finnaly, to ensure the attitude of these controls the simulations is presented in the Matlab/Simulink environment.</p>

With the development of wind power generation in recent years, several studies have dealt with the active and reactive power control of wind power systems, along with the quality of energy produced and the connection to distribution networks. In this context, this research proposes a new contribution to the field. The major objective of this work is the development of a nonlinear adaptive backstepping control technique applied to a DFIG based wind system and an optimization technique that uses the rooted tree optimization (RTO) algorithm. The backstepping control strategy is based on the Lyapunov nonlinear technique to guarantee the stability of the system. It is applied to the two converters (i.e., machine and network sides) and subsequently improved with estimators to make the proposed system robust to parametric variation. The RTO technique is based on monitoring the behavior of the underlying foundation of trees in search of underground water in accordance with the level of underground control. The solution proposed for the control is validated using two methods: (1) a simulation on MATLAB/Simulink to test the continuation of the reference (real wind speed) and the robustness of the system and (2) a real-time implementation on a dSPACE-DS1104 board connected to an experimental bench in a laboratory. Simulation and experimental results highlight the validation of the proposed model with better performance compared with other control techniques, such as sliding mode control, direct power control, and field-oriented control.

No abstract

<span lang="EN-US">This work is dedicated to the study of an improved direct control of powers of the doubly fed induction generator (DFIG) incorporated in a wind energy conversion system 'WECS'. The control method adopts direct power control 'DPC' because of its various advantages like the ease of implementation which allows decoupled regulation for active and reactive powers, as well as a good performance at transient and steady state without PI regulators and rotating coordinate transformations. To do this, the modeling of the turbine and generator is performed. Therefore, the Maximum Power Point Tracking (MPPT) technology is implemented to extract optimal power at variable wind speed conditions. Subsequently, an explanation of the said command is spread out as well as the principle of adjusting the active and reactive power according to the desired speed. Then, the estimation method of these two control variables will be presented as well as the adopted switching table of the hysteresis controller model used based on the model of the multilevel inverters. Finally, the robustness of the developed system will be analyzed with validation in Matlab / Simulink environment to illustrate the performance of this command.</span>