Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

Stéfano Frizzo Stefenon, Roberto Zanetti Freire, Leandro dos Santos Coelho, Luiz Henrique Meyer, Rafael Bartnik Grebogi, William Gouvêa Buratto and Ademir Nied
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Stéfano Frizzo Stefenon: Electrical Engineering Graduate Program, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, Brazil
Roberto Zanetti Freire: Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil
Leandro dos Santos Coelho: Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil
Luiz Henrique Meyer: Electrical Engineering Graduate Program, Regional University of Blumenau (FURB), Electrical Engineering, Blumenau 89030-000, Brazil
Rafael Bartnik Grebogi: Department of Computer Science, Federal Institute of Education Science and Technology of Santa Catarina (IFSC), Lages 88506-400, Brazil
William Gouvêa Buratto: Electrical Engineering Graduate Program, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, Brazil
Ademir Nied: Electrical Engineering Graduate Program, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, Brazil

Energies, 2020, vol. 13, issue 2, 1-19

Abstract: The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

Keywords: Adaptive Neuro-Fuzzy Inference System; insulator fault forecast; wavelet packets; time series forecasting (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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