State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks

TL;DR

This paper introduces a deep learning framework for topology identification and state estimation in unobservable distribution systems, improving accuracy with fewer sensors and robustness to noise.

Contribution

It presents a novel deep neural network approach for real-time topology and state estimation in partially observed distribution networks, including sensor placement strategies.

Findings

DNN-based DSSE outperforms traditional methods in accuracy.

The approach is robust against non-Gaussian measurement noise.

Fewer sensors are needed for reliable estimation.

Abstract

Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.