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Enriching Load Data Using Micro-PMUs and Smart Meters.
- Source :
- IEEE Transactions on Smart Grid; Nov2021, Vol. 12 Issue 6, p5084-5094, 11p
- Publication Year :
- 2021
-
Abstract
- In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. We have verified the proposed approach using real high- and low-resolution load data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19493053
- Volume :
- 12
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Smart Grid
- Publication Type :
- Academic Journal
- Accession number :
- 153811811
- Full Text :
- https://doi.org/10.1109/TSG.2021.3101685