Abstract
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measure-ments may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 - Washington, United States Duration: 16 Jan 2023 → 19 Jan 2023 |
Conference
Conference | 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 |
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Country/Territory | United States |
City | Washington |
Period | 16/01/23 → 19/01/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
NREL Publication Number
- NREL/CP-5D00-86284
Keywords
- Multi time-scale measurements
- Neural ordinary differential equations
- Smart distribution system
- variational autoencoder