A Portable System for the Evaluation of the Degree of Pollution of Transmission Line Insulators

Lucas de Paula Santos Petri, Emanuel Antonio Moutinho, Rondinele Pinheiro Silva, Renato Massoni Capelini, Rogério Salustiano, Guilherme Martinez Figueiredo Ferraz, Estácio Tavares Wanderley Neto, Jansen Paula Villibor and Suzana Silva Pinto
Additional contact information
Lucas de Paula Santos Petri: High Voltage Laboratory (Federal University of Itajubá—LAT-EFEI), Itajubá, MG 37500-903, Brazil
Emanuel Antonio Moutinho: Equatorial Energia, São Luís, MA 65070-900, Brazil
Rondinele Pinheiro Silva: Equatorial Energia, São Luís, MA 65070-900, Brazil
Renato Massoni Capelini: HVEX, Itajubá, MG 37502-508, Brazil
Rogério Salustiano: HVEX, Itajubá, MG 37502-508, Brazil
Guilherme Martinez Figueiredo Ferraz: HVEX, Itajubá, MG 37502-508, Brazil
Estácio Tavares Wanderley Neto: High Voltage Laboratory (Federal University of Itajubá—LAT-EFEI), Itajubá, MG 37500-903, Brazil
Jansen Paula Villibor: High Voltage Laboratory (Federal University of Itajubá—LAT-EFEI), Itajubá, MG 37500-903, Brazil
Suzana Silva Pinto: High Voltage Laboratory (Federal University of Itajubá—LAT-EFEI), Itajubá, MG 37500-903, Brazil

Energies, 2020, vol. 13, issue 24, 1-26

Abstract: Surface pollution is a major cause of partial discharges in high voltage insulators in coastal cities, leading to degradation of their surface and accelerating their aging process, which may cause visible arcing, flashovers and system faults. Thus, this work provides a methodology for the assessment of the condition of insulators based on an instrument which generates a severity degree to help the electric utility team schedule maintenance routines for the structures that really need it. The instrument uses a Raspberry Pi board as the processing core, a PicoScope oscilloscope for the data acquisition and an antenna as a partial discharge sensor. The algorithms are implemented in Python, and use artificial intelligence tools, such as a convolutional network and a fuzzy inference system. Laboratory test methods for the simulation of the field pollution conditions were successfully used for the validation of the instrument, which showed a good correlation between the pollution level and the severity degree generated. In addition to that, field collected data were also used for the evaluation of the proposed severity degree, which is demonstrated to be consistent when compared with the utility’s reports and the history of the selected areas from where data were collected.

Keywords: partial discharges; high voltage insulators; fault diagnosis; machine learning; convolutional neural networks; fuzzy inference system (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: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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