TY - GEN
T1 - Topology Identification with Smart Meter Data Using Security Aware Machine Learning
AU - Francis, Cody
AU - Rao, Vittal S.
AU - Trevizan, Rodrigo D.
N1 - Funding Information:
This work was supported by the U.S. Department of Energy, Office Electricity, Energy Storage program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering So-lutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy National Nuclear Security Administration under contract DE-NA-0003525.This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. SAND2021-12660 C.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Distribution system topology identification has historically been accomplished by unencrypting the information that is received from the smart meters and then running a topology identification algorithm. Unencrypted smart meter data introduces privacy and security issues for utility companies and their customers. This paper introduces security aware machine learning algorithms to alleviate the privacy and security issues raised with un-encrypted smart meter data. The security aware machine learning algorithms use the information received from the Advanced Metering Infrastructure (AMI) and identifies the distribution systems topology without unencrypting the AMI data by using fully homomorphic NTRU and CKKS encryption. The encrypted smart meter data is then used by Linear Discriminant Analysis, Convolution Neural Network, and Support Vector Machine algorithms to predict the distribution systems real time topology. This method can leverage noisy voltage magnitude readings from smart meters to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.
AB - Distribution system topology identification has historically been accomplished by unencrypting the information that is received from the smart meters and then running a topology identification algorithm. Unencrypted smart meter data introduces privacy and security issues for utility companies and their customers. This paper introduces security aware machine learning algorithms to alleviate the privacy and security issues raised with un-encrypted smart meter data. The security aware machine learning algorithms use the information received from the Advanced Metering Infrastructure (AMI) and identifies the distribution systems topology without unencrypting the AMI data by using fully homomorphic NTRU and CKKS encryption. The encrypted smart meter data is then used by Linear Discriminant Analysis, Convolution Neural Network, and Support Vector Machine algorithms to predict the distribution systems real time topology. This method can leverage noisy voltage magnitude readings from smart meters to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.
KW - Advanced metering infrastructure
KW - distribution system topology identification
KW - linear discriminant analysis
KW - security aware
UR - http://www.scopus.com/inward/record.url?scp=85124366350&partnerID=8YFLogxK
U2 - 10.1109/NAPS52732.2021.9654716
DO - 10.1109/NAPS52732.2021.9654716
M3 - Conference contribution
AN - SCOPUS:85124366350
T3 - 2021 North American Power Symposium, NAPS 2021
BT - 2021 North American Power Symposium, NAPS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 November 2021 through 16 November 2021
ER -