[Objective] The stability of the steam inlet system in solid oxide electrolysis cell(SOEC) systems is crucial for enhancing the electrolysis efficiency of the electrolytic stack and prolonging its service life. However

the nonlinear coupling between steam pressure and flow rate imposes high demands on the control strategy. [Methods] An experimental platform for the steam inlet system tailored for the 50 k W-class SOEC system was established to investigate the control of steam flow rate error and pressure fluctuation. Based on the collected operation data of the experimental platform

a double-delay deep deterministic policy gradient agent was trained. A multi-objective intelligent control(IC) strategy based on deep reinforcement learning was proposed

aiming to achieve the control goals of the system output flow error not exceeding 3% and pressure fluctuation not exceeding 1 kPa. [Results] The experimental results show that the maximum error of the output steam flow rate under the IC method is 1.4%

and the maximum pressure fluctuation is ±0.67 kPa. While under the PID control method

the maximum error of the steady-state output flow rate of the system is 3.8%

and the maximum fluctuation of the pressure is ±1.25 kPa. Compared with PID control

the IC method reduces the maximum flow rate error by 63.2% and the pressure fluctuation by 46.4%. [Conclusion] The proposed IC method demonstrates significantly superior control performance compared to the PID method.