T-S Fuzzy Sampled-Data LFC Scheme for Wind Power System via Improved Trapezoidal Algorithm

This paper studies the stability problem of sampled-data-based load frequency control (LFC) doubly fed induction generator (DFIG)-integrated wind power system (WPS). Firstly, a unified fuzzy DFIG-integrated WPS model is constructed by analyzing the nonlinear aspects of governor and turbine dynamics. Secondly, a sampled-data-based fuzzy proportional-integral control strategy (FPICS) is designed for stabilizing the power system. Thirdly, some new stability criteria are established by Lyapunov theory. Additionally, an improved trapezoidal algorithm is proposed to process the integral term in the controller, which can effectively reduce the consumption of computing resources. Finally, the effectiveness of proposed algorithm and the FPICS are verified through simulations. Note to Practitioners—Due to nonlinearities in the power system arising from the physical limitations of non-reheat governors, this paper aims to design an appropriate control strategy to address the nonlinear issues arising from governor valve position limiting, which may adversely affect the stability of the power system. Specifically, we propose an FPICS to tackle the nonlinearities caused by governor valves. Besides, an improved trapezoidal algorithm, which can dynamically determine and utilize the minimal number of subintervals according to the controller output variation, is designed to fit the numerical values of the controller’s integral term. This ensures control signal accuracy while maximizing computational resource savings. Moreover, to enhance the system’s tolerance to significant delays and disturbances, this paper considers the time-varying delay in the controller and establishes new stability criteria. Finally, through theoretical analysis and simulation cases, the effectiveness and reliability of the proposed fuzzy control strategy and improved trapezoidal algorithm are validated.