A Novel Approach to Arcing Faults Characterization Using Multivariable Analysis and Support Vector Machine

John Morales, Eduardo Muñoz, Eduardo Orduña and Gina Idarraga-Ospina
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John Morales: Carrera de Ingeniería Mecatrónica, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador
Eduardo Muñoz: Carrera de Ingeniería Mecatrónica, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador
Eduardo Orduña: Instituto de Energía Eléctrica, Universidad Nacional de San Juan, San Juan J5400ARL, Argentina
Gina Idarraga-Ospina: Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, Monterrey León C.P. 64460, Mexico

Energies, 2019, vol. 12, issue 11, 1-21

Abstract: Based on the Institute of Electrical and Electronics Engineers (IEEE) Standard C37.104-2012 Power Systems Relaying Committee report, topics related to auto-reclosing in transmission lines have been considered as an imperative benefit for electric power systems. An important issue in reclosing, when performed correctly, is identifying the fault type, i.e., permanent or temporary, which keeps the faulted transmission line in service as long as possible. In this paper, a multivariable analysis was used to classify signals as permanent and temporary faults. Thus, by using a simple convolution process among the mother functions called eigenvectors and the fault signals from a single end, a dimensionality reduction was determined. In this manner, the feature classifier based on the support vector machine was used for acceptably classifying fault types. The algorithm was tested in different fault scenarios that considered several distances along the transmission line and representation of first and second arcs simulated in the alternative transients program ATP software. Therefore, the main contribution of the analysis performed in this paper is to propose a novel algorithm to discriminate permanent and temporary faults based on the behavior of the faulted phase voltage after single-phase opening of the circuit breakers. Several simulations let the authors conclude that the proposed algorithm is effective and reliable.

Keywords: arcing fault identification; autoreclosure; relay; transient analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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