Optimal power flow computation method based on multi-task deep learning
US11436494B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 10, 2022 |
| Grant date | Sep 6, 2022 |
| Priority date | — |
| Expiry date | Apr 10, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S10/50
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An optimal power flow computation method based on multi-task deep learning is provided, which is related to the field of smart power grids. The optimal power flow computation method based on multi-task deep learning includes: acquiring state data of a power grid at a certain dispatching moment, and amplifying collected data samples by means of sampling to acquire training data; applying an optimization method to acquire dispatching solutions of the power grid in different sampling states, and acquiring labels; designing a deep learning neural network model, learning feasibility and an optimal solution of an optimal power flow computation problem separately, and outputting a feasibility determination and an optimal solution prediction; simultaneously training, tasks of the feasibility determination and the optimal solution prediction in the optimal power flow computation problem; and determining whether there is a feasible dispatching solution, and outputting an optimal dispatching solution or an early warning.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.