Wind turbine control has attracted increasing attention, driven in part by evolving challenges due to the growing size and complexity of wind turbines. Addressing these challenges and maximizing wind turbine power production requires the application of advanced nonlinear control methods. Sliding Mode Control (SMC) has emerged as a promising approach in this context. Recent studies have explored the integration of an integral term with SMC, called I-SMC. This technique has been shown to result in system responses that exhibit chattering phenomena with noticeable state errors. This study aimed to address these issues through the introduction of a novel controller known as Integral Backstepping SMC (IB-SMC). This study demonstrated that IBSMC not only ensured the stability of wind turbines but also outperformed other control strategies, even in the presence of disturbances of approximately 30% of the rated electromagnetic torque. To validate the effectiveness of the proposed controller, extensive simulation tests were carried out using MATLAB /Simulink software to evaluate the controller's responsiveness to rapid changes in conditions, as well as its robustness and overall performance. A comparison was carried out between the IBSMC and previous SMCs to evaluate their ability to reduce steady-state error and chattering.

This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system’s stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to $99.93\%$.