Abstract

The intermediate point enthalpy is a significant indicator for the steam temperature in thermal power unit, which has a great impact on the safety and economy in power unit. The intermediate point enthalpy control becomes more difficult in supercritical power unit than in subcritical unit because of controlled plant's characteristics of non-linearity, large inertia and coupling between different input. In this paper, a reinforcement learning method using proximal policy optimization algorithm is proposed to operate the feed water following control scheme for coordinated control system in supercritical unit. Experiment indicates that the proposed method can achieve satisfactory control performance compared with feed-forward decoupling control.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.