Abstract
An increase in the weather events can result in an increase in the frequency of grid faults, thereby leading to a decrease in electricity supply reliability. To develop a better performance model for outage forecasting, it is essential to consider various types of events for different complex weather conditions and constraints. A conventional event-driven forecasting algorithm is only appropriate for a single type of event scenario and unable to adapt effectively to multiple types of event scenarios concurrently. A multiple-constraints event-driven outage model that depends on a maximum entropy function, which transforms a complex event-driven outage problem into two continuous differential single-objective subproblems using the collaborative neural network (CONN) algorithm is proposed in this article. The CONN event-driven forecasting algorithm, validated by many experiments, successfully resolves the problem of the difficulty in obtaining very large complex weather events and outages data.
Published Version
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.
