On the Potential of Digital Twins for Distribution System State Estimation with Randomly Missing Data in Heterogeneous Measurements

TL;DR

This paper introduces a digital twin-based distribution system state estimation model that effectively handles random missing data in heterogeneous measurements, improving robustness and accuracy in real-world grid monitoring.

Contribution

It presents an interactive attention-based DSSE model with physics-informed data augmentation and transfer, advancing robustness against data loss in distribution system state estimation.

Findings

Accurately estimates voltage states with up to 40% missing data.

Demonstrates robustness and improved accuracy in real-world unbalanced distribution systems.

Validates effectiveness through case study on an 84-bus system.

Abstract

Traditional statistical optimization-based state estimation (DSSE) algorithms rely on detailed grid parameters and mathematical assumptions of all possible uncertainties. Furthermore, random data missing due to communication failures, congestion, and cyberattacks, makes these methods easily infeasible. Inspired by recent advances in digital twins (DTs), this paper proposes an interactive attention-based DSSE model for robust grid monitoring by integrating three core components: physical entities, virtual modeling, and data fusion. To enable robustness against various data missing in heterogeneous measurements, we first propose physics-informed data augmentation and transfer. Moreover, a state-of-the-art attention-based spatiotemporal feature learning is proposed, followed by a novel cross-interaction feature fusion for robust voltage estimation. A case study in a real-world unbalanced 84-bus distribution system with raw data validates the accuracy and robustness of the proposed DT model in estimating voltage states, with random locational, arbitrary ratios (up to 40% of total measurements) of data missing.