Quantum deep reinforcement learning for rotor side converter control of double-fed induction generator-based wind turbines

Abstract The control performance of conventional analytic algorithms for double-fed induction generator-based wind turbines is a fixed feature, which needs to be optimized by optimization processes. To avoid the optimization processes and update control strategies online, this paper proposes an online control algorithm based on the quantum process, deep belief networks, and reinforcement learning for double-fed induction generator-based wind turbines. The proposed approach is named quantum deep reinforcement learning, consisting of deep belief networks, one reinforcement learning framework, and multiple subsidiary reinforcement learning parts with quantum processes. The quantum deep reinforcement learning can update the control strategy online with general initialization for dynamic systems, avoid optimal local solutions, and predict the next systemic states of double-fed induction generator-based wind turbines. The proportional–integral–derivative, fractional-order proportional–integral–derivative, active disturbance rejection controller, reinforcement learning, and the quantum deep reinforcement learning are compared under four cases, i.e., variable wind speed with step magnitude, variable wind speed with sine magnitude, voltage dropout of power grids, and both variable wind speed with random magnitude and voltage dropout of power grids. Consequently, the proposed quantum deep reinforcement learning can obtain better control performance for double-fed induction generator-based wind turbines with smaller integrated absolute error, integral squared error, and integrated time-weighted absolute error of the control error than other compared methods.