Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10⁻³ (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10⁻⁴ (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.

This new paper describes an experimental investigation of a proportional-integral (PI) controller that uses feedback control to regulate an energy system that uses multi-rotor wind energy systems. Pulse width modulation (PWM) is used by the proposed controller to control the power of the doubly-fed induction generator controlled by direct power control (DPC), which is intended to regulate and control the inverter. The proposed strategy differs from the traditional DPC strategy. The proposed control was studied in the case of variable wind speed, where the MATLAB and Dspace 1104 environment was used to implement this proposed feedback PI (FPI) controller, with a comparison with the proposed control technique and some existing works. The suggested FPI controller outperforms the traditional controller and certain other controllers in terms of lowering energy ripples, overshoot, steady-state error (SSE), response time, and the total harmonic distortion (THD) of supplied system currents, as demonstrated by experimental and simulation results. The THD value of current was reduced by 64.86% and 69.44% in the two proposed tests compared to the traditional DPC technique. Also, the value of ripples and overshoot of active power was reduced compared to the DPC method by 95.42% and 90.86%, respectively, in the case of step wind speeds. Moreover, ripples and SSE of reactive powers compared to the DPC technique were reduced by 37.51% and 84.13%, respectively. These high ratios indicate the high performance of the proposed DPC-FPI technique in enhancing the features of the system in contrast to the conventional DPC technique.

The design of robust and precise control of obtaining maximum power yield is an important area of research in wind engineering. In the context of maximizing the amount of power extraction in wind energy conversion systems (WECS), this research work proposes and evaluates five MPPT algorithms. These types are respectively a proportional integral controller (PI), a non-linear control based on sliding modes (SMC), a backstepping approach (BSC), a control using artificial intelligence based on neural network (ANNC), and a fuzzy logic control (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the ANNC controller with an error static that not exceed 0.39% and a response time ~0.0024 seconds.

The super-twisting sliding mode controller (STSMC) is considered one of the simplest and easiest to implement nonlinear controls, as it can be easily applied in industrial systems. Using this controller leads to reducing the phenomenon of chatter and significantly increasing the durability of the systems. However, this controller has several drawbacks and problems, as its use does not lead to improving the systems as required. In this study, an idea has been proposed based on the use of neural networks in order to overcome the defects and problems of the STSMC strategy. This proposed strategy is used to increase the durability and improve the characteristics of an energy system based on wind turbines using a dual induction generator. The latter is controlled by direct power control (DPC). The proposed controller was used to control energies, where the pulse width modulation strategy was used to convert voltage reference values into pulses to operate the rotor side converter. Therefore, simplicity, durability, outstanding performance, ease of implementation, few gains, and low cost are among the most prominent features of the proposed intelligent STSMC technique and control. The MATLAB environment was used first to implement the proposed energy system under different working conditions using variable wind speed, where the proposed controller was compared with the performance of the STSMC technique. The proposed intelligent STSMC technique was implemented experimentally using the dSPACE 1104 Card to verify the performance and the validity of the simulated results. The experimental results show the effectiveness and ability of the proposed controller to improve the characteristics of the DPC strategy and the energy system as a whole.

Developing precise and robust algorithms that can help in obtaining maximum power yield in a variable speed wind turbine is an important area of research in wind engineering. The present manuscript proposes a technique that utilizes a second-generation CRONE controller for the maximum power tracking technique (MPPT) to maximize power generation in a wind energy conversion system (WECS) based on a double-fed induction generator (DFIG). The authors propose this novel method because the classical controllers cannot provide adequate performance in terms of extracting the maximum energy from variable speed wind turbines when applying a real wind profile and they cannot guarantee the high stability of the WECS. Moreover, this novel controller sufficiently handles problems related to the control effort level. The performance of the second-generation CRONE method was mathematically modeled using MATLAB/Simulink and compared with four other types of MPPT control techniques, which include a proportional-integral linear controller (PI), nonlinear sliding mode controller (SMC), backstepping controller (BS), and fuzzy logic controller (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the second-generation CRONE controller in terms of all of the compared parameters.