Abstract
This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the adaptive neural-based supervisory controller for the hybrid wind microgrid system. The simulation results show that the proposed adaptive neuralbased supervisory controller trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.
Highlights
Hybrid renewable energy systems can be classified into two main types: grid-connected and standalone
This paper proposes the novel use of adaptive neural MIMO model to generate the supervisory controller for the hybrid wind microgrid systems
The Back Propagation (BP) algorithm optimally generates the appropriate neural weightings to perfectly characterize the features of the supervisory controller for the hybrid wind microgrid systems
Summary
Hybrid renewable energy systems can be classified into two main types: grid-connected and standalone. This paper proposes the novel use of adaptive neural MIMO model to generate the supervisory controller for the hybrid wind microgrid systems.
Sign-in/Register to view highlights & summary 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.
