In this article, we investigate the robust stabilization for an interconnected power system with a doubly fed induction generator (DFIG)-based wind farm via retarded sampled-data control (RSDC). Generally, the interconnected power system with DFIG-based wind farm considers a mechanical torque, and load deviation, which is taken into disturbance of the proposed model. The main concern of this article is to stabilize and mitigate the frequency fluctuation, and speed deviation of the DFIG-based wind farm. To do this, a more general sampled-data control strategy, involving the effect of constant time delay is considered and the sampling period is assumed to vary within an interval. In addition, the defined disturbances are attenuated by using the $H_{\infty }$ performance-based RSDC scheme. An appropriate Lyapunov Krasovskii functional (LKF) is constructed to obtain the delay-dependent sufficient conditions in the form of linear matrix inequalities (LMIs) by using the RSDC strategy. The obtained conditions ensure the proposed closed-loop system is asymptotically stable under the designed controller. Finally, simulation results and comparative results are given to illustrate the effectiveness of the designed control scheme.

This study is concerned with the problem of maximum power extraction for a permanent magnet vernier generator (PMVG)-based wind energy conversion system (WECS) using the Q-learning maximum power point tracking (MPPT) algorithm with the finite-time control (FTC) scheme. To do this, the model-free sensorless reinforcement Q-learning algorithm-based MPPT method is firstly proposed. At this time, the reward and learning rate are updated by the Q-values for each state-action pair. Next, the proposed learning algorithm is used to construct an optimal speed–power curve for achieving the fast and steady MPPT operation for the WECS using the learned action values. Besides, the wind-speed change detection algorithm is added to the proposed method so that the PMVG-based WECS can work in various wind speed conditions. And then, the FTC method is proposed to track the reference speed of PMVG which support to achieving the maximum power extraction, and Lyapunov stability theory is derived to ensure the overall system's stability. Finally, the simulation and experimental results from the 5 kW PMVG-based WECS are presented to demonstrate the applicability and superiority of the proposed control method.

The power system network grows yearly with a large number of nonlinear power generation systems. In this scenario, accurate modeling, control, and monitoring of interface systems and energy conversion systems are critical to the reliability and performance of the overall power system. In this trend, the permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) equipped with a full-rated converter significantly contribute to the development of new and renewable energy generation. The various components and control systems involved in operating these systems introduce higher complexity, uncertainty, and highly nonlinear control challenges. To deal with this, state estimation remains an ideal and reliable procedure in the relevant control of the entire WTS. In essence, state estimation can be useful in control procedures, such as low-voltage ride-through operation, active power regulation, stator fault diagnosis, maximum power point tracking, and sensor faults, as it reduces the effects of noise and reveals all hidden variables. However, many advanced studies on state estimation of PMSG-based WTS deal with real-time information of operating variables through filters and observers, analysis, and summary of these strategies are still lacking. Therefore, this article aims to present a review of state-of-the-art estimation methods that facilitate advances in wind energy technology, recent power generation trends, and challenges in nonlinear modeling. This review article enables readers to understand the current trends in state estimation methods and related issues of designing control, filtering, and state observers. Finally, the conclusion of the review demonstrates the direction of future research.