Power grid assets prediction using generative adversarial networks

Patent metadata

Field	Value
Publication number	US-12046901-B1
Application number	US-202318166108-A
Country	US
Kind code	B1
Filing date	Feb 8, 2023
Priority date	Sep 17, 2019
Publication date	Jul 23, 2024
Grant date	Jul 23, 2024

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.

First claim

What is claimed is:

1. A method for using a neural network to predict locations of feeders in an electrical power grid, the method comprising:
providing input map data that includes input images as input to a generative adversarial network comprising a generator and a discriminator; and
generating, by the generator from the input map data, output map data with feeder metadata that represents predicted locations of feeder assets, including receiving, by the generator, a first input image and generating by the generator corresponding first output map data 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,
wherein the discriminator is trained based on training output images generated by the generator and ground truth images, where the ground truth images include respective ground truth feeder data identifying feeder assets and their locations on the ground truth images.

2. The method of claim 1 , wherein generating first output map data with first feeder data comprises also receiving, with the first input image corresponding input feeder metadata representing one or more feeder assets in the first input image.

3. The method of claim 2 , wherein generating the first output map data with first feeder data comprises also receiving with the first input image one or more asset placement rules.

4. The method of claim 3 , wherein:
the respective feeder data is incorporated in a respective output image.

5. The method of claim 3 , wherein:
the respective feeder data is generated as metadata separate from an output image.

6. The method of claim 1 , wherein generating the first output map data includes generating, as first feeder data, data that identifies an underground feeder asset including a location of the underground feeder asset.

7. The method of claim 6 , wherein the first feeder data identifies all feeder assets between a particular substation and a particular load.

8. The method of claim 7 , wherein the particular load is a residential load.

9. The method of claim 8 , wherein the one or more feeder assets comprise three or more of a line, a pole, a crossarm, a transformer, a switch, an insulator, a recloser, a sectionalizer, a capacitor bank, including switched capacitors, a load tap changer, or a tap.

10. The method of claim 9 , wherein the one or more feeder assets comprise a first transformer and the first feeder data specifies a size of the first transformer.

11. The method of claim 9 , wherein the one or more feeder assets comprise a first capacitor bank that includes switched capacitors.

12. The method of claim 1 , wherein the generator and discriminator are each a respective convolutional neural network model.

13. The method of claim 1 , wherein training the generative adversarial network comprises:
training the generator while holding the discriminator fixed, including
providing training input to the generator, the training input comprising training input map data, and
providing corresponding training outputs generated by the generator to the discriminator, the training outputs comprising map data including feeder metadata, and
training the generator based on a respective discriminator output from the discriminator for each training output received by the discriminator.

14. The method of claim 13 , wherein:
the training input includes, for a first plurality of training inputs, data representing respective identified above-ground feeder assets corresponding to training input images.

15. The method of claim 14 , wherein:
the training input includes one or more asset placement rules.

16. At least one non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
providing input map data that includes input images as input to a generative adversarial network comprising a generator and a discriminator; and
generating, by the generator from the input map data, output map data with feeder metadata that represents predicted locations of feeder assets, including receiving, by the generator, a first input image and generating, by the generator, corresponding first output map data 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,
wherein the discriminator is trained based on training output images generated by the generator and ground truth images, where the ground truth images include respective ground truth feeder data identifying feeder assets and their locations on the ground truth images.

17. A system comprising one or more computers configured to perform operations comprising:
providing input map data that includes input images as input to a generative adversarial network comprising a generator and a discriminator; and
generating, by the generator from the input map data, output map data with feeder metadata that represents predicted locations of feeder assets, including receiving, by the generator, a first input image and generating by the generator corresponding first output map data 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,
wherein the discriminator is trained based on training output images generated by the generator and ground truth images, where the ground truth images include respective ground truth feeder data identifying feeder assets and their locations on the ground truth images.

18. The system of claim 17 , wherein generating first output map data with first feeder data comprises also receiving with the first input image corresponding input feeder metadata representing one or more feeder assets in the first input image.

19. The system of claim 18 , wherein generating the first output map data with first feeder data comprises also receiving with the first input image one or more asset placement rules.

20. The system of claim 17 , wherein generating the first output map data includes generating, as first feeder data, data that identifies an underground feeder asset including a location of the underground feeder asset.

21. At least one non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
providing input map data that includes input images as input to training a generative adversarial network comprising a generator and a discriminator; and
generating, by the generator and from the input map data including input images, output map data with feeder metadata that represents predicted locations of feeder assets, including receiving, by the generator, a first input image and generating, by the generator, corresponding first output map data 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,
wherein the generator is trained based on training output generated from the discriminator from output map data generated by the generator responsive to generator training input, the output map data comprising electrical feeder metadata, and
wherein the discriminator was trained using grou

Assignees

• X Dev Llc

Inventors

• Stahlfeld Phillip Ellsworth
• Ni Bin

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• G06N3/0475

  Generative networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/094

  Adversarial learning · CPC title

• G06N3/09

  Supervised learning · CPC title