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:
Sep. 22, 2026

Filed:

Nov. 04, 2022
Applicant:

Hitachi, Ltd., Tokyo, JP;

Inventors:

Yanzhu Ye, San Jose, CA (US);

Bo Yang, Santa Clara, CA (US);

Panitarn Chongfuangprinya, San Jose, CA (US);

Masanori Abe, Mito, JP;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 30/18 (2020.01); G05B 13/02 (2006.01); G05B 13/04 (2006.01); G05B 23/02 (2006.01); G06F 18/21 (2023.01); G06F 30/27 (2020.01); G06F 30/3308 (2020.01); G06F 30/39 (2020.01); G06N 3/006 (2023.01); G06N 3/0442 (2023.01); G06N 3/08 (2023.01); G06N 3/09 (2023.01); G06N 20/00 (2019.01); G06Q 50/06 (2024.01); H02J 3/001 (2026.01);
U.S. Cl.
CPC ...
G06F 30/18 (2020.01); G06F 18/217 (2023.01); G06F 30/27 (2020.01); G06F 30/3308 (2020.01); H02J 3/001 (2020.01); G05B 13/0265 (2013.01); G05B 13/04 (2013.01); G05B 23/0283 (2013.01); G06F 30/39 (2020.01); G06N 3/006 (2013.01); G06N 3/0442 (2023.01); G06N 3/08 (2013.01); G06N 3/09 (2023.01); G06N 20/00 (2019.01); G06Q 50/06 (2013.01); Y04S 10/50 (2013.01);
Abstract

Systems and methods described herein can involve reducing a feeder system model by node cell segmentation on feeder system according to system topology information and system operation characteristics to generate a node cell segmented distribution grid; constructing observational data from systemwide status information aggregated from nodes identified from the node cell segmented distribution grid to meet a reinforcement learning (RL) policy network input requirement; training an RL policy framework to generate control actions for controllable components of the system; and executing the RL policy framework to generate control actions for controllable nodes in the node cell segmented distribution grid.


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