Skip to main content

ieee top header menu

IEEE.org
IEEE Xplore Digital Library
IEEE Standards
IEEE Spectrum
More Sites

Anonymous user menu

Cart
Create Account
Sign In

Conferences
ISGT

ISGT Middle East
ISGT Asia
ISGT Europe
ISGT Latin America
ISGT NA
ISGT
Grid / Power

Grid Edge
Grid Resilience
Power Africa
Power Con
PowerTech
Other Conferences

General Meeting
APPEEC
ESMO
GTD
PES Day
PSCE
SDG (ITSLC UN)
Student Chapter Committee
T&D
TESC
Young Professionals
Education
Live

Live Online
On-Demand

Plain Talks
Tutorials
Webinars
Interactive
Publications
Magazines

Electrification Magazine
Power and Energy Magazine
Papers

Open Access Papers: OAJPE Vol. 11 (2024)
Open Access Papers: OAJPE Vol. 10 (2023)
Open Access Papers: OAJPE Vol. 9 (2022)
Open Access Papers: OAJPE Vol. 8 (2021)
Open Access Papers: OAJPE Vol. 7 (2020)
Open Access Papers: PETS-J
Tutorial Papers
White Papers
Other Publications

Proceedings
Slides
Surveys
Technical Reports
Videos
Member Resources
Slides
Videos
Technical Committees
Slides
Tools
Videos
Leave this field blank

IEEE PES Resource Center

PES Home

carsten maple

A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

04 Jan 2022

A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

PES

Members: Free
IEEE Members: Free
Non-members: Free

Subscribe to carsten maple

Become a PES Member

Enhance your education and professional development. Join the IEEE Power & Energy Society to access webinars, conference presentations, workshops, and more on the PES Resource Center.
Get Started »

IEEE Home Contact & Support Accessibility Nondiscrimination Policy IEEE Ethics Reporting IEEE Privacy Policy Terms

© Copyright 2024 IEEE – All rights reserved. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.