Unsupervised non-technical losses identification through optimum-path forest-Reference-Cited by-同舟云学术

Unsupervised non-technical losses identification through optimum-path forest

Published:2016-11 Issue: Volume:140 Page:413-423
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Passos Júnior Leandro Aparecido,Oba Ramos Caio César,Rodrigues Douglas,Pereira Danillo Roberto,de Souza André Nunes,Pontara da Costa Kelton Augusto,Papa João Paulo

Funder

CAPES

FAPESP

CNPq

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference30 articles.

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2. Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system;Nagi;IEEE Trans. Power Deliv.,2011

3. Nontechnical loss detection for metered customers in power utility using support vector machines;Nagi;IEEE Trans. Power Deliv.,2010

4. Comparison of supervised learning techniques for non-technical loss detection in power utility;Yap;Int. Rev. Comput. Softw.,2012

5. A new approach for nontechnical losses detection based on optimum-path forest;Ramos;IEEE Trans. Power Syst.,2011

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