Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids

2022/11/07 by Shweta Dahale, Sai Munikoti, Dahale, Shweta +5
Computer Science · Engineering · Physics and Astronomy · #Energy Load and Power Forecasting #FOS: Electrical engineering #Model Reduction and Neural Networks #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2211.03882

openalex publication_date 2022/11/07 · openalex created_date 2022/11/14 · openalex updated_date 2026/07/28

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 measurements 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.

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