Fuzzy model predictive MPPT control of interconnected wind turbines drivetrain

Abstract

This paper provides a novel fuzzy‐based model predictive control (MPC) of a hydraulic‐based drivetrain that can combine the harvested energy of multiple wind turbines into one generator. The modeling is based on the sector nonlinearity approach and Takagi–Sugeno (T‐S) fuzzy. In addition, prediction and quadratic programming (QP) are formulated to obtain suitable control commands for the displacement of hydraulic pumps. Input–output linearization is utilized to control the generator torque load to regulate the speed. Minimizing the cost function for T‐S fuzzy‐based interconnected hydraulic wind turbine systems is converted to a QP problem to reduce the computation complexity. Therefore, the proposed nonlinear fuzzy‐based MPC scheme is more realistic to be implemented in real time. The feasibility of the optimization problem is improved when a new ellipsoidal terminal constraint is considered in the controller design process. Offline linear matrix inequalities (LMIs) are utilized to obtain the terminal cost of T‐S fuzzy‐based nonlinear systems. The simulation results demonstrate a significant reduction in computations and control efforts compared to LMI‐based MPC, kω2 approach, and (proportional–integral) PI controllers. It is also demonstrated that the system response to a sudden wind speed variation contains less overshoot and settles in a shorter time.

An Ingenious Technique to Track the Maximum Power Point for a Wind Energy System

“…Intelligent techniques like Fuzzy logic control (FLC) are found to be used in the literature [31] to overcome the issues associated with conventional methods like P&O. The work presented in [32] employs a model predictive control technique using FLC to track MPP. The work done in [33] presents an adaptive FLC technique for tracking the MPP.…”

Robust H∞ observer-based model predictive controller for uncertain linear discrete-time systems due to external disturbances

“…Compared with the work by Farbood et al (2022), Tao et al (2023), Farbood et al (2023) and Zhou et al (2022)), the controller is designed based on the estimation of the system states from the practical aspect.…”