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Event detection and classification through wavelet-based method in low voltage wide-area monitoring systems

Vaz, Rodrigo ; Moraes, Guido R. ; Arruda, Eduardo H.Z. ; Terceiro, Jyvago C.B.S. ; Aquino, Antonio F.C. ; Decker, Ildemar C. ; Issicaba, Diego

International journal of electrical power & energy systems, 2021-09, Vol.130, p.106919, Article 106919 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    Event detection and classification through wavelet-based method in low voltage wide-area monitoring systems
  • Autor: Vaz, Rodrigo ; Moraes, Guido R. ; Arruda, Eduardo H.Z. ; Terceiro, Jyvago C.B.S. ; Aquino, Antonio F.C. ; Decker, Ildemar C. ; Issicaba, Diego
  • Assuntos: Discrete wavelet transform ; Event analysis ; Phasor measurement units ; Wide area monitoring system
  • É parte de: International journal of electrical power & energy systems, 2021-09, Vol.130, p.106919, Article 106919
  • Descrição: •Detection and classification of systemic events based on the wavelet transform using low-voltage data.•Filtering and cross-checking mechanisms to reject local events and classify systemic events.•Parameter optimization using a Particle Swarm Optimization-based approach.•Validations with data from the Brazilian Interconnected System and Chilean Power System. This paper presents an approach to automatically analyse events on electric power systems using data from phasor measurement units installed at low voltage level. The proposed approach is based on the application of multiresolution analysis through the wavelet transform, decomposing signals into functions of both time and frequency domains, aiming to detect and characterize systemic events. Detection and classification algorithms are designed to recognize systemic events according to the energy extracted from wavelet detail coefficients, taking into account decomposition levels, thresholds and decision variables which are set using a particle swarm optimization algorithm. Numerical results, validated with low voltage phasor measurements of the Brazilian and Chilean power systems, show that the approach can be used effectively for event detection and classification.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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