Fast power system disturbance identification using enhanced LSTM network with renewable energy integration
US11176442B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 11, 2021 |
| Grant date | Nov 16, 2021 |
| Priority date | — |
| Expiry date | Feb 11, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S40/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A synchrophasor measurement-based disturbance identification method is described considering different penetration levels of renewable energy. A differential Teager-Kaiser energy operator (dTKEO)-based algorithm is first utilized to improve multiple-disturbances detection accuracy. Then, feature extractions via the integrated additive angular margin (AAM) loss and the long short-term memory (LSTM) network is described. This enables one to deal with intra-class similarity and inter-class variance of disturbances when high penetration renewable energy occurs. With the extracted features, a multi-stage weighted summing (MSWS) loss-based criterion is described for adaptive data window determination and fast disturbance pre-classification. Finally, the re-identification model based on feature similarity is established to identify unknown disturbances, a challenge for existing machine learning algorithms.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.