Power grid assets prediction using generative adversarial networks
US11152785B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 17, 2019 |
| Grant date | Oct 19, 2021 |
| Priority date | — |
| Expiry date | Apr 20, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S20/222
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a neural network to predict locations of feeders in an electrical power grid. One of the methods includes training a generative adversarial network comprising a generator and a discriminator; and generating, by the generator, from input images, output images with feeder metadata that represents predicted locations of feeder assets, including receiving by the generator a first input image and generating by the generator a corresponding first output image with first feeder data that identifies one or more feeder assets and their respective locations, wherein the one or more feeder assets had not been identified in any input to the generator.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.