The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 22, 2021

Filed:

Jun. 07, 2019
Applicant:

Battelle Memorial Institute, Richland, WA (US);

Inventors:

Xiaoyuan Fan, Richland, WA (US);

Xinya Li, Richland, WA (US);

Emily L. Barrett, Richland, WA (US);

Qiuhua Huang, Richland, WA (US);

James G. O'Brien, Richland, WA (US);

Renke Huang, Richland, WA (US);

Zhangshuan Hou, Richland, WA (US);

Ruisheng Diao, Richland, WA (US);

Assignee:

Battelle Memorial Institute, Richland, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G05B 13/04 (2006.01); G05B 13/02 (2006.01); H02J 3/24 (2006.01); G06Q 40/04 (2012.01); H02J 3/00 (2006.01);
U.S. Cl.
CPC ...
G05B 13/042 (2013.01); G05B 13/0265 (2013.01); G06Q 40/04 (2013.01); H02J 3/008 (2013.01); H02J 3/24 (2013.01);
Abstract

Techniques and apparatuses are described that enable transformative Remedial Action Scheme (RAS) analyses and methodologies for a bulk electric power system, including methods of designing, reviewing, revising, testing, implementing, verifying, or validating a RAS. An improved RAS improves operation of the power system, including performance, reliability, control, and asset utilization. The example methodologies discussed—also referred to as a transformative Remedial Action Scheme tool (TRAST)—provide an end-to-end solution for adaptively setting RAS parameters based on realistic and near real-time operation conditions to improve power grid reliability and grid asset utilization, by leveraging utility data analysis and employing dynamic simulations and machine learning to significantly simplify and shorten the entire RAS process.


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