A Probabilistic Optimum-Path Forest Classifier for Non-Technical Losses Detection
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Undergraduate course
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Ieee-inst Electrical Electronics Engineers Inc
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Article
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Acesso aberto
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Abstract
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/ or machines, for instance. Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios. In this paper, we propose a probabilistic-based optimum-path forest (OPF) classifier to handle the problem of non-technical losses (NTL) detection in power distribution systems. The proposed approach is compared against naive OPF, probabilistic support vector machines, and logistic regression, showing promising results for both NTL identification and in the context of general-purpose applications.
Description
Keywords
Optimum-path forest, probabilistic classification, non-technical losses
Language
English
Citation
Ieee Transactions On Smart Grid. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 3, p. 3226-3235, 2019.
