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Fault detection in low voltage networks with smart meters and machine learning techniques

Author:
Vázquez, Tania; Pérez Núñez, PabloUniovi authority; Díez Peláez, JorgeUniovi authority; Fernández, Jesús
Subject:

Preference Learning

Smart meter

Publication date:
2019-06-04
Editorial:

CIRED

Publisher version:
https://www.cired-repository.org/bitstream/handle/20.500.12455/170/CIRED%202019%20-%20851.pdf?sequence=1&isAllowed=y
Abstract:

Smart grid data analytics and artificial intelligence techniques are playing an increasingly critical role, becoming the focal point to understanding low voltage real-time grid performance. This new point of view, (advanced analytics in combination with electrical knowledge expertise), makes flexibility and efficiency in electrical grid management approach real. HDCE (Hidrocantábrico Distribución Eléctrica) is the Electrical Distribution System Operator for EdP (Electricity of Portugal) around Spain who supplies energy to 650.000 customers. Starting from 2012, this company has nowadays replaced 99% of traditional meters by smart meters. Based on the analysis of smart metering voltage alarms, recorded from EdP LV distribution network, an automatic learning system has been implemented that groups and orders these alarms helping the grid distribution operator to drive the network technicians to the right and more urgent places where a grid failure is happening, starts to happen or will happen.

Smart grid data analytics and artificial intelligence techniques are playing an increasingly critical role, becoming the focal point to understanding low voltage real-time grid performance. This new point of view, (advanced analytics in combination with electrical knowledge expertise), makes flexibility and efficiency in electrical grid management approach real. HDCE (Hidrocantábrico Distribución Eléctrica) is the Electrical Distribution System Operator for EdP (Electricity of Portugal) around Spain who supplies energy to 650.000 customers. Starting from 2012, this company has nowadays replaced 99% of traditional meters by smart meters. Based on the analysis of smart metering voltage alarms, recorded from EdP LV distribution network, an automatic learning system has been implemented that groups and orders these alarms helping the grid distribution operator to drive the network technicians to the right and more urgent places where a grid failure is happening, starts to happen or will happen.

Description:

25th International Conference on Electricity Distribution (CIRED 2019), junio, Madrid (Spain)

URI:
http://hdl.handle.net/10651/53588
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