An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor

• Autor(a) principal: Santos, Vitor Vecina dos [UNESP]
• Data de Publicação: 2020
• Outros Autores: Castro, Bruno Albuquerque de [UNESP], Binotto, Amanda [UNESP], Rey, Jorge Alfredo Ardila, Lucas, Guilherme Beraldi [UNESP], Andreoli, André Luiz [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/ecsa-7-08244; http://hdl.handle.net/11449/247575
• topic: non-destructive methods; partial discharge evolution; piezoelectric sensors; wavelet trasform

Under normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the dielectric degradation process is the presence of partial discharges (PD). Although it starts due to operational stresses, PD can cause a progressive insulation deterioration, since it is characterized by localized current pulses that emit heat, UV radiation, acoustic, and electromagnetic waves. In this sense, acoustic emission (AE) transducers are wildly applied in PD detection. The goal is to reduce maintenance costs by predictive actions and avoid total failures. Becasue of the progressive deterioration, the assessment of the PD evolution is crucial for improving the maintenance planning and ensure the operation of the transformer. Based on this issue, this article presents a new wavelet -based analysis in order to characterize the PD evolution. Three levels of failures were carried out in a transformer and the acoustic signals captured by a lead zirconate titanate piezoelectric transducer were processed by discrete wavelet transform. The experimental results revealed that the energy of the approximation levels increasing with the failure evolution. More specifically, levels 4, 6, 7, and 10 presented a linear fit to characterize the phenomena, enhancing the applicability of the proposed approach to transformer monitoring.