Abstract
This article presents a series of quality analysis methods for power flow samples to support artificial intelligence learning algorithms. These mechanisms extract adequate physical quantities from a large amount of flow physical information, simplifying the complexity of sample quality analysis. The proposed method can effectively evaluate the redundancy of power flow samples and suggests a practical design for generating representative flow samples. The complete mechanism includes quality analysis, physical information selection, and power flow sample dataset re-integration, which helps data-dependent AI algorithms to be more convenient and efficient in the application to power systems.
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE 6th International Electrical and Energy Conference, CIEEC 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3312-3317 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350346671 |
| DOIs | |
| State | Published - 2023 |
| Externally published | Yes |
| Event | 6th IEEE International Electrical and Energy Conference, CIEEC 2023 - Hefei, China Duration: 12 May 2023 → 14 May 2023 |
Publication series
| Name | 2023 IEEE 6th International Electrical and Energy Conference, CIEEC 2023 |
|---|
Conference
| Conference | 6th IEEE International Electrical and Energy Conference, CIEEC 2023 |
|---|---|
| Country/Territory | China |
| City | Hefei |
| Period | 12/05/23 → 14/05/23 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Power flow sample supplement
- current vector neighborhood grid clustering
- power flow sample quality assessment
- redundancy analysis
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