LEStabilization of permanent magnet synchronous generator-based wind turbine system via fuzzy-based sampled-data control approach

This study concerns the stabilization analysis for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine system under fuzzy-based memory sampled-data (FBMSD) control scheme. In this regard, the Takagi-Sugeno (T-S) fuzzy theory is utilized in the conversion of the proposed nonlinear model into linear-sub models and the corresponding FBMSD controller is designed. The paper introduces a suitable Lyapunov-Krasovskii functional that contains the information about the length of the sampling interval and a constant transmission delay. In order to stabilize the proposed system, sufficient conditions are derived in the form of linear matrix inequalities. As a test benchmark, the derived T-S fuzzy PMSG model is evaluated with a particular data set values and then validated with derived conditions. Besides that, the dynamical nature of system variables with respect to a blade pitch angle of the wind turbine system also discusses along with d - q axes inductance via numerical simulations. Finally, a comparison of derived conditions with the existing works is discussed to prove the less conservatism of the proposed method. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study focuses on the stabilization analysis of a nonlinear PMSG-based wind turbine system under a fuzzy-based memory sampled-data control scheme. The Takagi-Sugeno fuzzy theory is utilized to convert the nonlinear model into linear sub-models, and a FBMSD controller is designed using suitable Lyapunov-Krasovskii functional. The derived conditions are verified through numerical simulations and compared with existing works to demonstrate the less conservatism of the proposed method.