Unsupervised non-technical losses identification through optimum-path forest

详细信息    查看全文

• 作者：Leandro Aparecido Passos Jú ; nior^a ; ^{leandropassosjr@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Caio Cé ; sar Oba Ramos^c ; ; Douglas Rodrigues^a ; Danillo Roberto Pereira^b ; André ; Nunes de Souza^c ; Kelton Augusto Pontara da Costa^b ; Jo&atilde ; o Paulo Papa^b ; ^{papa@fc.unesp.br" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Non-technical losses ; Optimum-path forest ; Clustering ; Anomaly detection
• 刊名：Electric Power Systems Research
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：140
• 期：Complete
• 页码：413-423
• 全文大小：1846 K

文摘

<dl class="listitem" id="list_list0010"><dt class="label">•dt><dd>

d="p0005">A new method based on unsupervised OPF for NTL detection is proposed.

dd><dt class="label">•dt><dd>

d="p0010">Modelling the problem of NTL as being an anomaly detection task.

dd><dt class="label">•dt><dd>

d="p0015">Employing unsupervised OPF to estimate the parameters of the Gaussian distributions.

dd><dt class="label">•dt><dd>

d="p0020">An anomaly detection approach based on unsupervised optimum-path forest.

dd><dt class="label">•dt><dd>

d="p0025">Performing unsupervised OPF against the well-known unsupervised techniques.

dd>dl>