Robust sliding-Backstepping mode control of a wind system based on the DFIG generator

Nonlinear robust sliding mode - Backstepping hybrid control for WECS -theoretical design and experimental evaluation

This paper proposes a new contribution in the field of optimizing control techniques for wind systems to enhance the quality of the energy produced in the grid. Although the Sliding Mode control technique, whether classical or involving the use of artificial intelligence, has shown interesting results, its main drawback lies in the oscillation phenomenon commonly referred to as "chattering." This phenomenon affects the accuracy and robustness of the system, as well as the parametric variation of the system. In this work, we propose a solution that combines two nonlinear techniques based on the Lyapunov theorem to eliminate the chattering phenomenon. It is a hybrid approach between the Backstepping strategy and the Sliding Mode, aiming to control the active and reactive powers of the doubly fed induction generator (DFIG) connected to the electrical grid by two converters (grid side and machine side). This hybrid technique aims to improve the performance of the wind system in terms of precision errors, stability, as well as active and reactive power. The proposed solution has been validated in the Matlab & Simulink environment to assess the performance and robustness of the proposed model, as well as experimentally validated on a test bench using the DSPACE 1104 card. The obtained results are then compared with other techniques, demonstrating a significant improvement in performance.

Self-filtering based on the fault ride-through technique using a robust model predictive control for wind turbine rotor current

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy.

Application of fractional-order synergetic-proportional integral controller based on PSO algorithm to improve the output power of the wind turbine power system

It is noted that the traditional direct filed-oriented control (DFOC) is widely used in the field of electric power generation from wind due to its fast response dynamic, ease of implementation and simplicity, but this strategy is characterized by the presence of large ripples at the level of both active and reactive powers. This work presents a new algorithm for DFOC strategy of an asynchronous generator (AG) in a wind power (WP) system, which is based on the use of a new nonlinear controller called fractional-order synergetic control-fractional-order proportional-integral (FOSC-FOPI) controller, where the proposed technique parameters are calculated using the particle swarm optimization (PSO) strategy. It has been observed that the DFOC-FOSC-FOPI-PSO strategy is robust and works well in case of changing generator parameters. Three tests were performed to study the behavior of the designed technique under different working conditions, where the behavior of the DFOC-FOSC-FOPI-PSO strategy was compared with the behavior of the traditional DFOC technique in terms of power ripple ratio, overshoot, steady-state error, response time, tracking reference, and current quality. The simulation of the designed technique based on the FOSC-FOPI-PSO strategy of the AG-WP system is carried out using Matlab software, where the simulation results showed that the suggested technique is better than the classical technique (with PI controller) in terms of improving response time of active power (33.33%) and reactive power (10%) in second test, reduction of the steady-state error of reactive power (96.95%) and active power (97.14) in first test, minimization of harmonic distortion of current (96.57%) in third test and significant minimization of ripples of active power (99.67%, 44.69%, and 98.95%) and reactive power (99.25%, 53.65%, and 70.50%) in the three tests. The effectiveness of the DFOC-FOSC-FOPI-PSO strategy is very high, so it can be a reliable solution for controlling various generators.

Low-voltage ride-through capability in a DFIG using FO-PID and RCO techniques under symmetrical and asymmetrical faults

The power grid faults study is crucial for maintaining grid reliability and stability. Understanding these faults enables rapid detection, prevention, and mitigation, ensuring uninterrupted electricity supply, safeguarding equipment, and preventing potential cascading failures, ultimately supporting the efficient functioning of modern society. This paper delves into the intricate challenge of ensuring the robust operation of wind turbines (WTs) in the face of fault conditions, a matter of substantial concern for power system experts. To navigate this challenge effectively, the implementation of symmetrical fault ride-through (SFRT) and asymmetrical fault ride-through (AFRT) control techniques becomes imperative, as these techniques play a pivotal role in upholding the stability and dependability of the power system during adverse scenarios. This study addresses this formidable challenge by introducing an innovative SFRT-AFRT control methodology based on rotor components optimization called RCO tailored for the rotor side converter (RSC) within a doubly-fed induction generator (DFIG) utilized in wind turbine systems. The proposed control strategy encompasses a two-fold approach: firstly, the attenuation of both positive and negative components is achieved through the strategic application of boundary constraints and the establishment of reference values. Subsequently, the optimization of the control characteristic '[Formula: see text]' is accomplished through the utilization of a particle swarm optimization (PSO) algorithm integrated within an optimization loop. This intricate interplay of mechanisms aims to optimize the performance of the RSC under fault conditions. To measure the efficacy of the proposed control technique, a comparative analysis is conducted. Fractional-order (FO) proportional-integral-derivative (PID) controllers are employed as an additional method to complement the novel approach. By systematically juxtaposing the performance of the proposed SFRT-AFRT control technique with the FO-PID controllers, a comprehensive evaluation of the proposed approach's effectiveness is attained. This comparative assessment lends valuable insights into the potential advantages and limitations of the novel control technique, thereby contributing to the advancement of fault mitigation strategies in WT systems. Finally, the paper highlights the economic viability of the proposed control method, suggesting its suitability for addressing broader power network issues, such as power quality, in future wind farm research.