Data Mining-Based Method to Reduce Multiple Estimation for Fault Location in Radial Distribution Systems

This paper presents an approach to reduce the multiple estimation effect of fault location algorithms. This effect occurs in fault location techniques based on fault distance estimation concerning radial distribution feeders. This approach is based on a data mining technique called data mining of co...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2019-07, Vol.10 (4), p.3612-3619
Main Authors: Reche, Evandro Agostinho, Sousa, Jeovane Vicente de, Coury, Denis Vinicius, Fernandes, Ricardo Augusto Souza
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Computer simulation
DAMICORE
Data mining
Estimation
Fault location
Feature extraction
Feeders
Measuring instruments
multiple estimation
Radial distribution
Repair & maintenance
Repositories
Smart grid
Smart grids
Smart meters
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Summary:This paper presents an approach to reduce the multiple estimation effect of fault location algorithms. This effect occurs in fault location techniques based on fault distance estimation concerning radial distribution feeders. This approach is based on a data mining technique called data mining of code repositories (DAMICORE). This tool is executed from the perspective of cloud computing in the context of smart grids. It is noteworthy that the voltage and current signals are received by a cloud using smart meters and disturbance recorders. Thus, this cloud receives a feature vector that is extracted by them from the signals acquired. Considering this, the cloud becomes responsible for executing the DAMICORE which, in turn, defines relations among the faulty events. The IEEE 34-Bus Test Feeder was simulated as the test case system. Moreover, the data mining process was able to reduce errors due to the multiple estimation of faulty branches.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2018.2832840