TY - GEN
T1 - Advances in decentralized state estimation for power systems
AU - Li, Xiao
AU - Scaglione, Anna
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Distributed learning via network diffusion is a popular trend in signal processing, which addresses the need of obtaining scalable analytics from networked sensor systems. This paper describes relevant advances in distributed power system state estimation (PSSE) via diffusion. Considering a hybrid sensor measurements system, we show that the Gauss-Newton approach, typically favored in PSSE, can be used as a primitive to derive a gossip-based algorithm that outperforms first order diffusion methods proposed in the literature. We also study analytically and numerically the dependency between measurement placement, grid topology and physical parameters, communication network and the performance of the decentralized PSSE.
AB - Distributed learning via network diffusion is a popular trend in signal processing, which addresses the need of obtaining scalable analytics from networked sensor systems. This paper describes relevant advances in distributed power system state estimation (PSSE) via diffusion. Considering a hybrid sensor measurements system, we show that the Gauss-Newton approach, typically favored in PSSE, can be used as a primitive to derive a gossip-based algorithm that outperforms first order diffusion methods proposed in the literature. We also study analytically and numerically the dependency between measurement placement, grid topology and physical parameters, communication network and the performance of the decentralized PSSE.
UR - http://www.scopus.com/inward/record.url?scp=84894146490&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894146490&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2013.6714099
DO - 10.1109/CAMSAP.2013.6714099
M3 - Conference contribution
AN - SCOPUS:84894146490
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 428
EP - 431
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -