Abstract
AbstractThe multi-agent system approach to power plant control provides many benefits in that it allows multiple computationally intensive tasks to be performed in parallel to provide effective real-time control, such as training and adapting artificial neural network models, parameter optimization, and system monitoring. The multi-agent system approach also provides intelligent control that is robust and flexible in that it can autonomously make decisions depending on the situation and adjust to partial control system failure to maintain control, to name a few of the potential benefits. It is the goal of this paper to detail the implementation of real-time feedback gain optimization as a means to provide more efficient control and demonstrate an added advantage of the parallel nature of a multi-agent system designed for intelligent optimized multiobjective 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 callDisclaimer: 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.
