The coupling between the steam turbine and boiler in supercritical units is severe

and the dynamic response varies greatly.The traditional proportional integral differential(PID)coordinated control is difficult to balance the stability of the main steam pressure and rapid load response.To this end

a control strategy based on the twin delayed deep deterministic policy gradient(TD3)algorithm is proposed.Through the introduction of the double Critic evaluation network and the delayed policy update

The training instability issue of the deep deterministic policy gradient(DDPG)algorithm has been effectively addressed

which enhances the control quality of the coordinated control system. Firstly

the improved seagull optimization algorithm is applied to conduct model identification of the unit coordination system

and establishes mathematical models of the controlled object;Secondly

the initial exploration mechanism of TD3 algorithm is improved based on action-enhancing strategies(AES)to speed up the convergence speed of the algorithm. The multi-dimensional state information and multiobjective reward function are designed

and a control framework centered on the TD3 agent is constructed.The optimal fuel control command is generated through TD3 algorithm to improve the response speed and control accuracy of the main steam pressure on the boiler side.Finally

the simulink simulation based on 600 MW supercritical unit shows that this strategy can effectively improve the control quality.