Understanding causes of low voltage (LV) faults in electricity distribution network using association rule mining and text clustering

Silva, HCE and Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 2019, Understanding causes of low voltage (LV) faults in electricity distribution network using association rule mining and text clustering , in: 3RD IEEE Industrial and Commercial Power System Europe (I&CPS), 11-14 June 2019, Genoa, Italy.

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Abstract

In-depth understanding of a fault cause in electricity distribution network has always been of paramount importance to Distributed Network Operators (DNO) for a reliable power supply. Faults in the network have direct effect on its stability, availability and maintenance; and so, their quick elimination, prevention and avoidance of fault causes that generated them, is of special interest. Possible opportunity to understanding the causes and correlation of the factors where future faults may arise can significantly help electricity distribution operators who happen to be accountable to detect and repair such problems. Every asset in the distribution network has a different level of reliability and which may vary. Faults identifying in distribution network have rich literature but a very few studies had been done on understanding the factors that contribute to LV Faults using data mining and machine learning techniques. As there are lack of studies on Faults identifying in distribution network with data mining, this study will formulate a starting point. This paper aims to use the association rule mining and clustering techniques to understand the various hidden patterns from the faults database. The uncovered relationships can be represented in the form of Association rules and clusters. The outcomes of this research will hugely beneficial to the engineering departments in DNOs. New knowledge gain from this study will help to priorities investments in new or replacement infrastructure which will ensure that financial and manpower resources are used more efficiently.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings, 3RD IEEE Industrial and Commercial Power System Europe (I&CPS)
Publisher: IEEE
ISBN: 9781728106533; 9781728106540; 9781728106526
Related URLs:
Depositing User: Prof. Mo Saraee
Date Deposited: 04 Apr 2019 10:07
Last Modified: 14 Nov 2019 15:22
URI: http://usir.salford.ac.uk/id/eprint/50965

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