Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

Govindasamy Narayanan, Sangtae Ahn, Yong Wang, Jae Hoon Jeong, Young Hoon Joo

Abstract: Highlights•In this study, we have constructed a fractional-order PMSG-based state estimator model that incorporates the AET-based SDC scheme and accounts for the stochastic nonlinear phenomena of the network estimator. Based on this estimator model, we have developed a novel closed-loop fractional-order PMSG model that utilizes IF-THEN fuzzy rules to enhance performance.•This study aims to design an AET-based SDC as an adaptive condition that determines whether the sampled signals should be sent to the controller. Additionally, the threshold value in the AET condition can be dynamically adjusted based on both previously and currently transmitted signals.•By combining fractional Lyapunov stability theory with stochastic analysis based on the Bernoulli distribution, we establish sufficient conditions through LMIs to ensure that the dynamics of the estimation error are ultimately exponentially bounded in the mean square. Furthermore, the estimator’s gain matrices are formulated using singular value decomposition (SVD) and the LMI technique.•Finally, the proposed control method for the fractional-order PMSG-based WES is validated through numerical simulations, and comparisons with the fractional-order Chua’s circuit demonstrate its enhanced effectiveness and emphasizing the advantages of the proposed approach.