Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network

• Using a new control method to harvest the maximum power from wind energy system.
• Using an adaptive control scheme based on quantum neural network (QNN).
• Improving of MPPT-TSR method by direct adaptive control scheme based on QNN.
• Improving of MPPT-OT method by indirect adaptive control scheme based on QNN.
• Using a windmill system based on PMSG to evaluate proposed control schemes.

In this paper, a quantum neural network (QNN) is used as controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID controller and conventional neural network controller (CNNC).