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Title: Deep Neural Network-based State Estimator for Transmission System Considering Practical Implementation Challenges
As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate 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, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.  more » « less
Award ID(s):
2145063
PAR ID:
10565108
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
SGEPRI
Date Published:
Journal Name:
Journal of Modern Power Systems and Clean Energy
Volume:
12
Issue:
6
ISSN:
2196-5625
Page Range / eLocation ID:
1810 to 1822
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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