Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges

TL;DR

This paper introduces DeNSE, a deep neural network-based state estimator that combines SCADA and PMU data within a Bayesian framework to achieve real-time full system state estimation despite practical challenges.

Contribution

It presents a novel deep learning approach that integrates diverse data sources for accurate, real-time power system state estimation considering implementation issues.

Findings

DeNSE outperforms traditional SCADA and PMU estimators in accuracy.

The approach is effective under topology changes and noisy measurements.

Scalability to large systems is demonstrated with a 2000-bus model.

Abstract

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.