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

Author

Listed:
  • 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
    Department of Electrical Engineering, Federal University of Parana (UFPR), Curitiba 81530-000, 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)

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.

Suggested Citation

  • Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:484-:d:310524
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    References listed on IDEAS

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    Cited by:

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    2. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    3. Cristina Keiko Yamaguchi & Stéfano Frizzo Stefenon & Ney Kassiano Ramos & Vanessa Silva dos Santos & Fernanda Forbici & Anne Carolina Rodrigues Klaar & Fernanda Cristina Silva Ferreira & Alessandra Ca, 2020. "Young People’s Perceptions about the Difficulties of Entrepreneurship and Developing Rural Properties in Family Agriculture," Sustainability, MDPI, vol. 12(21), pages 1-12, October.
    4. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    5. Rafael Ninno Muniz & Stéfano Frizzo Stefenon & William Gouvêa Buratto & Ademir Nied & Luiz Henrique Meyer & Erlon Cristian Finardi & Ricardo Marino Kühl & José Alberto Silva de Sá & Brigida Ramati Per, 2020. "Tools for Measuring Energy Sustainability: A Comparative Review," Energies, MDPI, vol. 13(9), pages 1-27, May.
    6. Chin-Tan Lee & Shih-Cheng Horng, 2020. "Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree," Energies, MDPI, vol. 13(10), pages 1-19, May.
    7. Ariel Vieira de Oliveira & Márcia Cristina Schiavi Dazzi & Anita Maria da Rocha Fernandes & Rudimar Luis Scaranto Dazzi & Paulo Ferreira & Valderi Reis Quietinho Leithardt, 2022. "Decision Support Using Machine Learning Indication for Financial Investment," Future Internet, MDPI, vol. 14(11), pages 1-17, October.
    8. Luqman Maraaba & Khaled Al-Soufi & Twaha Ssennoga & Azhar M. Memon & Muhammed Y. Worku & Luai M. Alhems, 2022. "Contamination Level Monitoring Techniques for High-Voltage Insulators: A Review," Energies, MDPI, vol. 15(20), pages 1-32, October.
    9. Tariq Kamal & Murat Karabacak & Vedran S. Perić & Syed Zulqadar Hassan & Luis M. Fernández-Ramírez, 2020. "Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid," Energies, MDPI, vol. 13(18), pages 1-22, September.
    10. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.

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