Stability and Stabilization for T–S Fuzzy Large-Scale Interconnected Power System With Wind Farm via Sampled-Data Control

TLDR: This article focuses on the stability and stabilization analysis of large-scale multiarea interconnected power systems (LSMAIPSs) involving the wind farm through the Lyapunov stability theory, where the Takagi–Sugeno (T–S) fuzzy model is able to achieve better performance.

Abstract: This article focuses on the stability and stabilization analysis of large-scale multiarea interconnected power systems (LSMAIPSs) involving the wind farm through the Lyapunov stability theory. Instead of linearizing the nonlinear model at a certain operating point the Takagi–Sugeno (T–S) fuzzy model is able to achieve better performance. In this article, a doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) are integrated into each area of the power system. The main reason behind the integration is because the power factor has the ability to destabilize the performance of the power supply. To ensure the stability of the LSMAIPS, a decentralized sampled-data feedback load frequency control is designed. The stability and stabilization conditions are derived through constructing suitable Lyapunov function which contains the sampling information and the solvable linear matrix inequalities (LMIs) along with an evaluation of $H_{\infty }$ performance. To an evident, the simulation results are performed based on experimental values of two-area large-scale interconnected power system with DFIG-based wind farm, which guarantees the asymptotic stability of the proposed T–S fuzzy system under the sampled-data controller.