Large Language Model-aided Edge Learning in Distribution System State Estimation

TL;DR

This paper introduces a novel edge learning framework for distribution system state estimation that leverages large language models for forecasting missing data, improving robustness and accuracy in real-time monitoring.

Contribution

It proposes a forecast-then-estimate framework using LLMs and multi-task learning to enhance DSSE accuracy under data missing conditions.

Findings

LLMs effectively forecast missing measurements in DSSE.

The multi-task learning approach reduces overfitting.

Numerical simulations validate the framework's effectiveness.

Abstract

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To address these challenging issues, a forecast-then-estimate framework of edge learning is proposed for DSSE, leveraging large language models (LLMs) to forecast missing measurements and provide pseudo-measurements. Firstly, natural language-based prompts and measurement sequences are integrated by the proposed LLM to learn patterns from historical data and provide accurate forecasting results. Secondly, a convolutional layer-based neural network model is introduced to improve the robustness of state estimation under missing measurement. Thirdly, to alleviate the overfitting of the deep learning-based DSSE, it is reformulated as a multi-task learning framework containing shared and task-specific layers. The uncertainty weighting algorithm is applied to find the optimal weights to balance different tasks. The numerical simulation on the Simbench case is used to demonstrate the effectiveness of the proposed forecast-then-estimate framework.