An Adaptive Single Neural Control for Variable Step-Size P&O MPPT of Marine Current Turbine System

Marine current energy has been increasingly used because of its predictable higher power potential. Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine (MCT) system, the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’ uncertainties. This paper proposes an adaptive single neural control (ASNC) strategy for variable step-size perturb and observe (P&O) maximum power point tracking (MPPT) control. Firstly, to automatically update the neuron weights of SNC for the nonlinear systems, an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients. Secondly, aiming to generate the exact reference speed for ASNC to extract the maximum power, a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT. The robust stability of the MCT control system is guaranteed by the Lyapunov theorem. Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions, and the MCT system operates at maximum power point steadily.