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.

中文翻译：

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基于采样数据控制的含风电场的TS模糊大型互联电力系统的稳定性和稳定性

本文着重于通过Lyapunov稳定性理论对涉及风电场的大型多区域互联电力系统（LSMAIPSs）的稳定性和稳定性进行分析。Takagi-Sugeno（TS）模糊模型能够在特定工作点上线性化非线性模型，而不是实现更高的性能。在本文中，将基于双馈感应发电机（DFIG）的风力涡轮机系统（WTS）集成到电力系统的每个区域中。集成背后的主要原因是因为功率因数具有破坏电源性能的能力。为了确保LSMAIPS的稳定性，设计了分散的采样数据反馈负载频率控制。 $ H _ {\ infty} $ 表现。显然，仿真结果是基于具有DFIG的风电场的两区域大型互联电力系统的实验值进行的，这保证了所提出的T–S模糊系统在采样数据控制器下的渐近稳定性。