# Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges

arXiv: https://arxiv.org/abs/2212.01729 · HTML: https://mlsanity.com/paper/2212.01729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 4 Dec 2022.

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

- [Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning](https://mlsanity.com/paper/1811.02756/) (2018)
- [The Creation and Validation of Load Time Series for Synthetic Electric Power Systems](https://mlsanity.com/paper/1911.06934/) (2019)
- [State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks](https://mlsanity.com/paper/2104.07208/) (2021)
- [Parameter Transfer Unit for Deep Neural Networks](https://mlsanity.com/paper/1804.08613/) (2018)
- [Robust and Scalable Power System State Estimation via Composite Optimization](https://mlsanity.com/paper/1708.06013/) (2017)
- [A Micro-PMU Placement Scheme for Distribution Systems Considering Practical Constraints](https://mlsanity.com/paper/2008.00354/) (2020)
- [Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise](https://mlsanity.com/paper/2208.13105/) (2022)
- [Statistical Characterization of Random Errors Present in Synchrophasor Measurements](https://mlsanity.com/paper/2103.09285/) (2021)

## Abstract (arXiv)

As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on the higher voltage buses. However, this causes many of the lower voltage levels of the bulk power system to not be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. We propose a Deep Neural network-based State Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but select PMU data to attain sub-second situational awareness of the entire system. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, and bad data detection and correction. The results obtained using the IEEE 118-bus system show the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus Synthetic Texas system.