1. Introduction
Nowadays, significant effort is being made to find a source of production of renewable energy as an alternative resource to secure fossil fuels while protecting the environment [
1]. Wind energy technology has received considerable attention in recent years in this context, owing to its many benefits, such as low cost, ease of deployment, and maintenance [
2,
3].
Despite the advantages of the wind energy conversion system, it suffers from instability and nonlinearity, resulting from the fluctuating nature of the wind, which can create some problems in the grid, such as a shock. To overcome these problems, the system requires robust controllers that can enable it to face the internal parametric changes and external disturbances and also achieve adequate performance under different operation conditions. For these reasons, several studies and algorithms have been applied to enhance the performance of WECS [
4,
5,
6]. The vector control strategy based on the classic PI controller is considered one of the well-known controls of (WECS) that are used to solve the current–voltage coupling problem in the system. However, this control is sensitive to the parametric changes of the machine.
For this reason, other controls have been proposed in the literature. According to [
7], sliding mode control (SMC) is a suitable approach for controlling the DFIG because of its durability. However, it suffers from chattering phenomena. Alami et al. [
8] proposed direct power control (DPC), which is characterized by its detachment from the internal parameters of the machine, but the hysteresis comparators remain the major drawback of this control. To overcome all deficiencies discussed previously, the nonlinear Backstep** approach is chosen based on its performances, simple implementation, and robustness. A good tracking response is also ensured, and the system’s stability is obtained by employing the Lyapunov function [
9,
10].
Backstep** operates using a switching-table-based algorithm to regulate the active and reactive power. Although it provides better control over the decoupling between the active and reactive power and simple algorithm implementation, it suffers from high power ripples, which can reduce the signal’s quality distributed to the grid [
11]. To avoid all these problems, several scientific researchers have proposed a series of nonlinear control methods to improve the robustness of the studied system [
12].
This article discusses the control design of DFIG. The particularity of this study is that it presents a novel controller structure that is distinct from the majority of sliding-mode-control-based PMSG wind turbine systems. The controller can sustain steady transient performance in the presence of external disturbances, handle any change in the wind speed rapidly and smoothly, and enhance the quality of the electrical energy delivered. Additionally, the validation of the proposed control was analyzed according to the stability, robustness, rapidity, and efficiency of the system, as well as the signal quality sent to the grid.
As a result, the hybrid drive is developed in this article to improve the dynamic power response of the DFI generator and minimize the ripples of its currents when injected into the grid. Furthermore, the planned control diagram is built in real-time using the Nexys 2 FPGA board to verify the experimental model.
The primary contributions of this work include develo** the Backstep** control rule, which is based on Lyapunov’s theorem; ensuring decoupling between the DFIG command variables; and improving system efficiency and robustness. Experimental validation of the approach proposed using the real-time interface connected to the Nexys 2 FPGA board is also included. For this purpose, this article is divided as follows: presentation of the model of the wind turbine system conversion chain; modeling and design of the adaptive Backstep** control technique; validation of the model proposed on Matlab/Simulink and also by a co-simulation with an implementation on the FPGA target; and finally, analysis and interpretation of the results.
3. Hybrid Control
The primary goal of our adaptative Backstep** approach is to run the wind turbine at full mechanical power. This requires checking the stator’s active powers
Ps and reactive powers
Qs (Equation (6)) according to [
16,
17]:
Because of the coupling between the active and reactive power, it is evident that the dynamic model Equation (8) is strongly nonlinear. For a study of this equation, use the Lyapunov function [
18,
19], which is divided into two steps:
Control of the speed [
20]:
Control of the powers:
where
To ensure the stability of the system, it is necessary to guarantee the negativity of the derivative of the Lyapunov (
V1 and
V2) function. For this, we define a positive constant “
k” in the derivative of Equations (9) and (10), such that
After the mathematical calculation, we consider the active and reactive powers as virtual inputs:
We also consider the control’s laws of the real machine:
where
KΩ > 0,
KPs > 0, and
KQs > 0.
We get the negativity of the derivative
V1 and
V2:
This equation shows the asymptotic stability of the origin in the equations of the system of the DFI generator.
4. FPGA Implementation
To adjust the functionality of the nonlinear control algorithm, we created a functional model for the adaptive Backstep** control using the ** control blocks: The first block is for controlling the active and reactive power of the stator and the second for the control laws Vrd and Vrq.
Calculation block: This block is used to calculate from the measured currents and voltages: the active power, the reactive power, the magnetic fluxes of the stator, the rotor pulsation, and the stator pulsation.
Measuring block: This block contains ADC interfaces that allow the connection between the FPGA and the analog-to-digital converter, which allows the currents to be acquired from a Hall Effect sensor.
PWM block: This block is used to generate the control signals Sa, Sb, and Sc of the rotor side converters. The Timing block controls the start and end of each block, which makes it possible to refresh the reference voltages at the start of each sampling period.