TY - JOUR
T1 - Low voltage current estimation using AMI/smart meter data
AU - Poursharif, G.
AU - Brint, A.
AU - Holliday, J.
AU - Black, M.
AU - Marshall, Mark
PY - 2018/7
Y1 - 2018/7
N2 - Knowledge of the currents is a key foundation for smart grid applications. However, knowledge of low voltage currents is generally poor. The new information streams from advanced metering infrastructure (AMI)/smart meters and the monitoring of distribution substations offer the opportunity of rectifying this. Unfortunately, often not all the smart meter readings will be available in real-time. For example, this situation will arise when older (non-compliant) smart meters do not have real-time reporting capabilities. This paper investigates how knowledge of the substation currents can be combined with the available real-time AMI/smart meter readings and the historical readings from the non-real-time meters, to estimate these missing values. It is found that the k-nearest neighbor weighted average approach performs best but that the gains over using simpler methods are relatively modest.
AB - Knowledge of the currents is a key foundation for smart grid applications. However, knowledge of low voltage currents is generally poor. The new information streams from advanced metering infrastructure (AMI)/smart meters and the monitoring of distribution substations offer the opportunity of rectifying this. Unfortunately, often not all the smart meter readings will be available in real-time. For example, this situation will arise when older (non-compliant) smart meters do not have real-time reporting capabilities. This paper investigates how knowledge of the substation currents can be combined with the available real-time AMI/smart meter readings and the historical readings from the non-real-time meters, to estimate these missing values. It is found that the k-nearest neighbor weighted average approach performs best but that the gains over using simpler methods are relatively modest.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85041483528&partnerID=MN8TOARS
UR - https://www.sciencedirect.com/science/article/pii/S0142061516313023?via%3Dihub
U2 - 10.1016/j.ijepes.2018.01.023
DO - 10.1016/j.ijepes.2018.01.023
M3 - Article
SN - 0142-0615
VL - 99
SP - 290
EP - 298
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
ER -