6 research outputs found
Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges
As phasor measurement units (PMUs) are usually placed on the highest voltage
buses, many lower voltage levels of the bulk power system are not observed by
them. This lack of visibility 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 local 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, a
SCADA-PMU hybrid state estimator, and a PMU-only linear state estimator from a
techno-economic viability perspective. Lastly, the scalability of the DeNSE is
proven by performing state estimation on a large and realistic 2000-bus
Synthetic Texas system