Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise

TL;DR

This paper presents a deep learning-based method for state estimation in distribution systems with limited measurements, demonstrating improved accuracy and robustness over classical methods under realistic noise conditions.

Contribution

It introduces a novel deep neural network approach for unbalanced three-phase distribution system state estimation with incomplete observations and realistic measurement noise.

Findings

DNN-based DSSE outperforms classical linear estimation in accuracy.

The method requires fewer synchrophasor measurement devices.

Robustness is validated with realistic measurement error models.

Abstract

Time-synchronized state estimation is a challenge for distribution systems because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach to perform unbalanced three-phase distribution system state estimation (DSSE). Initially, a data-driven approach for judicious measurement selection to facilitate reliable state estimation is provided. Then, a deep neural network (DNN) is trained to perform DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs). Robustness of the proposed methodology is demonstrated by considering realistic measurement error models for SMDs. A comparative study of the DNN-based DSSE with classical linear state estimation indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs.