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This paper proposes a nonlinear optimal Backstepping method for controlling the doubly-fed induction generator used in wind energy conversion and obtainingmaximum power. The grid is connected directly to the stator. The rotor, on the other hand, is connected to the grid via two bidirectional converters. This work aims to regulate active and reactive power while maintaining a unit power factor using the proposed controller. The Lyapunov function guarantees the stability of the system. The most challenging aspect of Backstepping is determining the best positive constants, which are critical to the system performance. This process becomes more complex, especially when the generator parameters are uncertain or when the wind profile varies. As a result, optimizing the gains is an essential aspect of the controller design. The particle swarm optimization method is suggested for determining the optimum Backstepping constants. The performance and robustness of the proposed method are investigated and compared to the Conventional Backstepping and the Proportional-Integral Control strategies of a 5 MW wind power plant system under parameter variations and quickly changing wind speed profiles. Matlab/Simulink makes it possible to get results. The advised methodology ensures the tracking system's robust stability and reduces the response time to 1.8 (m s). Furthermore, it guarantees a negligible static error. [ABSTRACT FROM AUTHOR]

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology. [ABSTRACT FROM AUTHOR]

• Establish the MPPT with optimal speed ratio combined with hidden layer recurrent neural network (HLRNN) to obtain faster convergence. • Optimize the PI controller gains by using a hidden layer recurrent neural network. • Investigate the active power behavior in the presence of unbalanced grid faults. • Amelioration of the response time of the produced power compared with some other existing neural network schemes. This paper suggests the Hidden Layer Recurrent Neural Network (HLRNN) for controlling a wind power generation system based on a dual-fed induction generator. The generator's stator is connected directly to the electrical grid, while the rotor is linked through bidirectional converters. A PI controller-based Indirect Vector Control (IVC) scheme has been established to pilot the system. The PI regulator allows linear systems to perform well, but when subjected to physical variation conditions, the system's response becomes unstable, making the PI controller insufficient. This paper aims to ensure the PI controller gains self-adaptation regardless of the severity of the circumstances. The effectiveness of the proposed control is demonstrated by taking into account critical conditions such as wind speed changes, generator parameter variations, and asymmetrical faults in the grid. Furthermore, it is confirmed by the enhanced performance and the reduced oscillations during a voltage dip. The simulation results, achieved using MATLAB/Simulink, demonstrate that the response time is reduced to 1.8 (m s), the static error is minimized to 0.16%, and the overshoot is improved to 0.24% when compared to the IVC based on the PI controller and some existing neural network schemes. [ABSTRACT FROM AUTHOR]