TY - GEN
T1 - Distributed Bayesian Estimation with Low-rank Data
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
AU - Ramakrishna, Raksha
AU - Scaglione, Anna
AU - Spanias, Andreas
AU - Tepedelenlioglu, Cihan
N1 - Funding Information:
This project was funded in part by the ASU SenSIP Center and the NSF I/UCRC award 1540040 and by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DEAR0000696. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV installations within a single ZIP code. Additionally, we also derive a Bayesian lower bound on the shading parameter's mean squared estimation error. The results are promising and show that we can successfully estimate the fraction of partial shading in solar installations that can usually go unnoticed.
AB - In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV installations within a single ZIP code. Additionally, we also derive a Bayesian lower bound on the shading parameter's mean squared estimation error. The results are promising and show that we can successfully estimate the fraction of partial shading in solar installations that can usually go unnoticed.
KW - Bayesian estimation
KW - Distributed array processing
KW - partial shading
KW - solar panel monitoring
UR - http://www.scopus.com/inward/record.url?scp=85068985418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068985418&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682854
DO - 10.1109/ICASSP.2019.8682854
M3 - Conference contribution
AN - SCOPUS:85068985418
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4440
EP - 4444
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 May 2019 through 17 May 2019
ER -