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

arXiv: https://arxiv.org/abs/2104.07208 · HTML: https://mlsanity.com/paper/2104.07208/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Neural Network with Unbounded Activation Functions is Universal Approximator](https://mlsanity.com/paper/1505.03654/) (2015)
- [Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning](https://mlsanity.com/paper/1811.02756/) (2018)
- [Physics-Aware Neural Networks for Distribution System State Estimation](https://mlsanity.com/paper/1903.09669/) (2019)
- [A Time-Series Distribution Test System Based on Real Utility Data](https://mlsanity.com/paper/1906.04078/) (2019)
- [Parameter Transfer Unit for Deep Neural Networks](https://mlsanity.com/paper/1804.08613/) (2018)
- [Joint Estimation of Operational Topology and Outages for Unbalanced Power Distribution Systems](https://mlsanity.com/paper/1912.04543/) (2019)
- [A Micro-PMU Placement Scheme for Distribution Systems Considering Practical Constraints](https://mlsanity.com/paper/2008.00354/) (2020)
- [Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise](https://mlsanity.com/paper/2011.04272/) (2020)

## Built on by (most cited first, 5 of 5)

- [Learning Distribution Grid Topologies: A Tutorial](https://mlsanity.com/paper/2206.10837/) (2022)
- [Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation](https://mlsanity.com/paper/2311.06973/) (2023)
- [Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks](https://mlsanity.com/paper/2408.06776/) (2024)
- [Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges](https://mlsanity.com/paper/2212.01729/) (2022)
- [Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks](https://mlsanity.com/paper/2311.12166/) (2023)

## Abstract (arXiv)

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.