skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Chen, Jianshu"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; (, International Conference on Artificial Intelligence and Statistics (AISTATS) 2023)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, 55th Asilomar Conference on Signals, Systems, and Computers)
    Full Text Available 
  3. ; ; (, IEEE Transactions on Smart Grid)
    Full Text Available 
  4. ; ; (, Proceedings of the 2018 IEEE Data Science Workshop)
    Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A “Learning-to-Infer” variational inference method is employed for efficient inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. As the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification. The Learning-to-Infer method is extensively evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance and scalability of the method in identifying arbitrary 
    more » « less
    Full Text Available