This paper proposed a physics-constrained twin delayed deep deterministic policy gradient (TD3) algorithm for simultaneous virtual inertia and damping control of a grid-connected variable speed doubly-fed induction generator (DFIG) wind turbine using a combined deep reinforcement learning (DRL) and quadratic programming as a novel solution to suppress frequency fluctuations caused by the control mechanism which decouples the active power from the system frequency, thus hiding the rotating kinetic energy of the wind generator. The optimization stage modifies the action of the DRL agent, thus preventing the agent from taking certain unsafe actions. We tested the effectiveness of the proposed scheme under various scenarios through simulations on an IEEE 9-bus test system in MATLAB/Simulink. Compared with other virtual inertia controls, the results show that the proposed scheme achieved improved dynamic performance with the lowest system frequency deviation and fastest frequency recovery under wind and load variations and severe grid faults. A further test on the IEEE 39-bus system shows that the grid size does not affect the performance of our proposed technique.
Note to Practitioners
—Integrating the wind turbine systems into the utility grid results in power quality problems such as frequency fluctuation, voltage dip, power loss, and severe power outages. The unpredictability and uncontrollability of the wind pose a serious problem in integrating wind energy conversion systems. This problem becomes worse with the increasing number of connected wind turbines. Therefore, new control strategies are required to mitigate this issue. The droop-based virtual and damping control method traditionally provides frequency support in grid-tied wind turbine systems. However, the fixed droop gain is a significant drawback of this method. In this paper, we proposed a novel physics-constrained deep-reinforcement learning-based virtual inertial and damping control. The proposed control agent is constrained from unsafe actions and rewarded for maintaining the grid frequency within operational limits. Simulation results with the IEEE 9 bus system validated the feasibility and effectiveness of our proposed approach. A comparison of our method with the conventional control scheme, adaptive droop-based virtual inertia control, etc., carried out under various operational scenarios verified the enhanced performance of our proposed strategy.