Abstract:

This study investigates an inertial flywheel system based on electromagnetic couplers. The topological structure and principle of the inertial flywheel system are first introduced, and the advantages of using electromagnetic couplers are highlighted, followed by mathematical modeling of the system. The traditional fixed-parameter proportional-integral-derivative (PID) control mode exhibits significant output power fluctuations during sudden active command changes. To address this limitation, this study proposes a variable-parameter PID active power control strategy for inertial flywheels based on reinforcement learning (RL). The proposed method uses an RL algorithm without a model reference to train the neural network RL Agent, which dynamically adjusts the PID parameters. The neural network processes four input variables: active power deviation, differentiation of the active power, rotational speed, and acceleration, while outputting optimized parameters P, I, and D. The PID parameters dynamically adapt to the changes in the system state. To verify the feasibility of the control strategy and the advantages of the control performance, the control strategy was compared with the traditional fixed-parameter PID control method on the MATLAB/Simulink simulation platform. The simulation results demonstrate that the P and I parameters in the variable-parameter PID control strategy significantly change when the system receives an active power adjustment instruction, resulting in corresponding adjustments in the output torque reference values. As a result, the overshoot and fluctuation of the system power output are reduced, and the dynamic response performance is improved.

Key words: flywheel energy storage, electromagnetic coupler, reinforcement learning, vector control, PID control