Abstract
Real-time monitoring and control of smart grids (SGs) is critical to the enhancement of reliability and operational efficiency of power utilities. We develop a real-time anomaly detection framework, which can be built based upon smart meter (SM) data collected at the consumers' premises. The model is designed to detect the occurrence of anomalous events and abnormal conditions at both lateral and customer levels. We propose a generative model for anomaly detection that takes into account the hierarchical structure of the network and the data collected from SMs. We also address three challenges existing in SG analytics: 1) large-scale multivariate count measurements; 2) missing points; and 3) variable selection. We present the effectiveness of our approach with numerical experiments.
| Original language | English (US) |
|---|---|
| Article number | 7908945 |
| Pages (from-to) | 5820-5830 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Smart Grid |
| Volume | 9 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2018 |
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Keywords
- anomaly detection
- big data
- response count data
- Smart grid analytics
ASJC Scopus subject areas
- Computer Science(all)
Cite this
A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. / Moghaddass, Ramin; Wang, Jianhui.
In: IEEE Transactions on Smart Grid, Vol. 9, No. 6, 7908945, 01.11.2018, p. 5820-5830.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A hierarchical framework for smart grid anomaly detection using large-scale smart meter data
AU - Moghaddass, Ramin
AU - Wang, Jianhui
PY - 2018/11/1
Y1 - 2018/11/1
N2 - Real-time monitoring and control of smart grids (SGs) is critical to the enhancement of reliability and operational efficiency of power utilities. We develop a real-time anomaly detection framework, which can be built based upon smart meter (SM) data collected at the consumers' premises. The model is designed to detect the occurrence of anomalous events and abnormal conditions at both lateral and customer levels. We propose a generative model for anomaly detection that takes into account the hierarchical structure of the network and the data collected from SMs. We also address three challenges existing in SG analytics: 1) large-scale multivariate count measurements; 2) missing points; and 3) variable selection. We present the effectiveness of our approach with numerical experiments.
AB - Real-time monitoring and control of smart grids (SGs) is critical to the enhancement of reliability and operational efficiency of power utilities. We develop a real-time anomaly detection framework, which can be built based upon smart meter (SM) data collected at the consumers' premises. The model is designed to detect the occurrence of anomalous events and abnormal conditions at both lateral and customer levels. We propose a generative model for anomaly detection that takes into account the hierarchical structure of the network and the data collected from SMs. We also address three challenges existing in SG analytics: 1) large-scale multivariate count measurements; 2) missing points; and 3) variable selection. We present the effectiveness of our approach with numerical experiments.
KW - anomaly detection
KW - big data
KW - response count data
KW - Smart grid analytics
UR - http://www.scopus.com/inward/record.url?scp=85055433390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055433390&partnerID=8YFLogxK
U2 - 10.1109/TSG.2017.2697440
DO - 10.1109/TSG.2017.2697440
M3 - Article
AN - SCOPUS:85055433390
VL - 9
SP - 5820
EP - 5830
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
SN - 1949-3053
IS - 6
M1 - 7908945
ER -