1 / 11

Identification of partial discharge signals

Identification of partial discharge signals . Marcus de Paula University of Wisconsin – Madison 12/13/2013. Background. Partial Discharges: Localized dielectric breakdown of a small portion of a solid or fluid electrical insulation system under high voltage stress ;

zonta
Download Presentation

Identification of partial discharge signals

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Identification of partial discharge signals Marcus de Paula University of Wisconsin – Madison 12/13/2013

  2. Background • Partial Discharges: • Localized dielectric breakdown of a small portion of a solid or fluid electrical insulation system under high voltage stress; • Can lead to loss of insulating capacity and electrical system failure.

  3. Background • Filtering problem: • Have the frequency spectrum close to the noise spectrum; • It requires more elaborate filtering method.

  4. Goal • Use the wavelet transform and a spatially-adaptive coefficient selection procedure to explore the localized processing capabilities of the WT as a way to improve the separation of coefficients related to the signal and noise.

  5. Goal • The process basically consists of 6 steps: • 1. Decomposition of the signal into 6 levels using WT. • 2. Extraction of each decomposition. • 3. Construction of the Maxima Lines. • 4. CLASSIFY lines associated with the signal or noise. • 5. Delete rows associated with noise. • 6. Rebuild signal using the remaining lines.

  6. Maxima Lines

  7. Training Data • Source: • Example:

  8. SVM classifier • Harmonic noise test: • Confusion matrix • Classification rate • Pulse noise test: • Confusion matrix • Classification rate • Real sample test: • Confusion matrix • Classification rate

  9. SVM classifier • Results:

  10. Future work • Use the MLP classifier; • Compare the results; • Analyze differences.

  11. References • [1] MOTA, H., Sistema de aquisição e tratamento de dados para monitoramento e diagnóstico de equipamentos elétricos pelo método das descargas parciais (Acquisition system and data processing for monitoringanddiagnosticofelectricalequipmentbythemethodofpartialdischarges). Universidade Federal de Minas Gerais (UFMG), ElectricalEngineeringGraduateProgram. Belo Horizonte, Minas Gerais, Brazil, Marchof 2001. • [2] MOTA, H., Processamento de sinais de descargas parciais em tempo real com base em wavelets e seleção de coeficientes adaptativa espacialmente (Signalprocessingofpartialdischarges in real time basedonwaveletsandselectionofspatiallyadaptivecoefficients). Universidade Federal de Minas Gerais (UFMG), ElectricalEngineeringGraduateProgram. Belo Horizonte, Minas Gerais, Brazil, Novemberof 2011.

More Related