Abstract
Often the accuracy or reliability of the time stamps of power quality data coming from different measurement locations is not adequate for certain analysis tasks. This paper studies improving the accuracy and reliability of the time stamps by utilizing the mutual correlations of the measured signals. Especially the electricity grid fundamental frequency estimated using a reliable and stable high performance adaptive method is almost the same everywhere in a synchronized power system. That enables accurate identification of clock time differences and drifts. The paper compares the results with the time stamps given by time managed local metered data preprocessing and concentrator servers. The results show that the identified time differences agree well with the time stamps given by the servers, but also reveals that the identified timing difference makes it possible to detect and correct the large timing errors that occasionally appeared in the time stamps added by the server.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665435970 |
| DOIs | |
| Publication status | Published - 28 Jun 2021 |
| Publication type | A4 Article in conference proceedings |
| Event | IEEE PowerTech - Duration: 28 Jun 2021 → 2 Jul 2021 |
Publication series
| Name | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings |
|---|
Conference
| Conference | IEEE PowerTech |
|---|---|
| Period | 28/06/21 → 2/07/21 |
Funding
This research is part of the Analytics project (2019-2023) funded by the Academy of Finland under Grant 324675.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
Keywords
- Adaptive signal processing
- Clock synchronization
- Frequency measurement
- Power quality
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Artificial Intelligence
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
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