Low voltage current estimation using AMI/smart meter data. (July 2018)

• Record Type: Journal Article
• Authors: Poursharif, Goudarz; Brint, Andrew; Holliday, John; Black, Mary; Marshall, Mark
• Is Part Of: International journal of electrical power & energy systems. Volume 99(2018)
• Page Start: 290
• Page End: 298
• Publication Date: 2018-07
• Subjects: Load allocation -- Smart grids -- Nearest neighbors -- Low voltage networks; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.01.023
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Filling in any gaps in the smart meter data, e.g. because of a time delay in receiving the readings from the older meters. Predicting currents on the low voltage network based on a load prediction for the distribution transformer. Using substation readings and historical smart meter data to predict missing smart meter readings. Abstract: 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.