TY - JOUR
T1 - State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools
AU - Dabush, Lital
AU - Kroizer, Ariel
AU - Routtenberg, Tirza
N1 - Funding Information:
This research was supported by the Israel Ministry of National Infrastructure, Energy, and Water Resources and by the BGU Cyber Security Research Center. L. Dabush is a fellow of the Ariane de Rothschild Women’s Doctoral Program.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to (Formula presented.) in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.
AB - This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to (Formula presented.) in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.
KW - graph signal processing (GSP)
KW - network observability
KW - power system state estimation (PSSE)
KW - sensor allocation
UR - http://www.scopus.com/inward/record.url?scp=85147893459&partnerID=8YFLogxK
U2 - 10.3390/s23031387
DO - 10.3390/s23031387
M3 - Article
C2 - 36772425
AN - SCOPUS:85147893459
SN - 1424-3210
VL - 23
JO - Sensors
JF - Sensors
IS - 3
M1 - 1387
ER -