Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges
This addresses the problem of situational awareness in bulk power systems for grid operators, offering a techno-economically viable solution, though it appears incremental as it builds on existing data fusion methods.
The paper tackles the challenge of achieving time-synchronized state estimation for full power systems with limited PMU coverage by proposing DeNSE, a deep neural network-based estimator that combines SCADA and PMU data, demonstrating superiority over existing methods on the IEEE 118-bus system and scalability on a 2000-bus system.
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.