Fuzzy Supervisory Passivity-Based High Order-Sliding Mode Control Approach for Tidal Turbine-Based Permanent Magnet Synchronous Generator Conversion System

Abstract

Higher efficiency, predictability, and high-power density are the main advantages of a permanent magnet synchronous generator (PMSG)-based hydro turbine. However, the control of a PMSG is a nontrivial issue, because of its time-varying parameters and nonlinear dynamics. This paper suggests a novel optimal fuzzy supervisor passivity-based high order sliding-mode controller to address problems faced by conventional techniques such as PI controls in the machine side. An inherent advantage of the proposed method is that the nonlinear terms are not canceled but compensated in a damped way. The proposed controller consists of two main parts: the fuzzy gain supervisor-PI controller to design the desired dynamic of the system by controlling the rotor speed, and the fuzzy gain-high order sliding-mode control to compute the controller law. The main objectives are feeding the electrical grid with active power, extracting the maximum tidal power, and regulating the reactive power and DC voltage toward their references, whatever the disturbances caused by the PMSG. The main contribution and novelty of the present work consists in the new robust fuzzy supervisory passivity-based high order sliding-mode controller, which treats the mechanical characteristics of the PMSG as a passive disturbance when designing the controller and compensates it. By doing so, the PMSG tracks the optimal speed, contrary to other controls which only take into account the electrical part. The combined high order sliding-mode controller (HSMC) and passivity-based control (PBC) resulted in a hybrid controller law which attempts to greatly enhance the robustness of the proposed approach regardless of various uncertainties. Moreover, the proposed controller was also validated using a processor in the loop (PIL) experiment using Texas Instruments (TI) Launchpad. The control strategy was tested under parameter variations and its performances were compared to the nonlinear control methods. High robustness and high efficiency were clearly illustrated by the proposed new strategy over compared methods under parameter uncertainties using MATLAB/Simulink and a PIL testing platform.