An evaluation of meta-heuristic approaches for improve the separation of multiple partial discharge sources and electrical noise

Elizabeth Montero, Nicolas Medina, Jorge Ardila-Rey

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

Currently, one of the most common methods of assessing the state of high voltage electrical equipment is to measure the activity of the partial discharges (PD) that may occur in it. Usually, most commercial measurement systems show the PD activity as a representation of pulses superimposed on a diagram of the network signal. These plots are called Phase-Resolved Partial Discharge patterns (PRPD), and are used to classify PD sources (corona, internal and surface). However, in common scenarios found in industrial environments, the identification of the type of source is practically impossible with the PRPD patterns, due to the presence of multiple PD sources and electrical noise which can create complex PRPD patterns even for an expert in the field. This challenge can be easily addressed, with the prior application of source separation techniques. In this paper, we propose the application of two meta-heuristic approaches, in order to automatize and improve the performance of the separation technique called spectral power clustering technique (SPCT), which is currently applied in the separation of PD sources and noise.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2017 International Conference on Tools with Artificial Intelligence, ICTAI 2017
EditorialIEEE Computer Society
Páginas1166-1173
Número de páginas8
Volumen2017-November
ISBN (versión digital)9781538638767
DOI
EstadoPublicada - 4 jun 2018
Evento29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017 - Boston, Estados Unidos
Duración: 6 nov 20178 nov 2017

Serie de la publicación

NombreProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volumen2017-November
ISSN (versión impresa)1082-3409

Conferencia

Conferencia29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017
País/TerritorioEstados Unidos
CiudadBoston
Período6/11/178/11/17

Áreas temáticas de ASJC Scopus

  • Software
  • Inteligencia artificial
  • Informática aplicada

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