Abstract
In this paper, an adaptive artificial fuzzy Mamdani-based model predictive approach for load frequency regulation of an isolated microgrid (IMG) is presented. A generalized state-space representation of an IMG comprising power sources is derived and exploited to predict the future behavior and control inputs for the IMG in order to regulate the frequency. The considered adaptive MPC is on the basis of a conventional model predictive approach that is equipped by a fuzzy logic tuning mechanism. A rule-based fuzzy logic is considered to tune the weight of the cost function in the model predictive controller (MPC), which highly impacts the minimization of the frequency deviation in the system. To show the advantages of the fuzzy adaptive MPC, it is compared with a conventional MPC in which the weights of the cost function is chosen by trial and error and fixed during the simulation. OPAL Real-time (RT) results show that the closed-loop system response performance based on the suggested fuzzy MPC is enhanced for different scenarios of the IMG load frequency system.
Sign-in/Register to access full text options 
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.
