IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p484-d310524.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/484/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/2/484/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francisco Martínez-Álvarez & Alicia Troncoso & Gualberto Asencio-Cortés & José C. Riquelme, 2015. "A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting," Energies, MDPI, vol. 8(11), pages 1-32, November.
    2. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    3. Enwen Li & Linong Wang & Bin Song & Siliang Jian, 2018. "Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data," Energies, MDPI, vol. 11(9), pages 1-17, September.
    4. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    5. Samuel Atuahene & Yukun Bao & Yao Yevenyo Ziggah & Patricia Semwaah Gyan & Feng Li, 2018. "Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change," Energies, MDPI, vol. 11(10), pages 1-19, October.
    6. Mishari Metab Almalki & Constantine J. Hatziadoniu, 2018. "Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees," Energies, MDPI, vol. 11(3), pages 1-16, March.
    7. Roberto Zanetti Freire & Gerson Henrique dos Santos & Leandro dos Santos Coelho, 2017. "Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines," Energies, MDPI, vol. 10(8), pages 1-16, July.
    8. S. Hr. Aghay Kaboli & Amer Al Hinai & A.H. Al-Badi & Yassine Charabi & Abdulrahim Al Saifi, 2019. "Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm," Energies, MDPI, vol. 12(19), pages 1-18, September.
    9. Ying-Yi Hong & Yan-Hung Wei & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2014. "Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System," Energies, MDPI, vol. 7(4), pages 1-18, April.
    10. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.
    11. Fang Liu & Ranran Li & Aliona Dreglea, 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model," Energies, MDPI, vol. 12(18), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    4. 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.
    5. 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.
    6. 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.
    7. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    8. Georgios Falekas & Athanasios Karlis, 2021. "Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-26, September.
    9. 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.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.
    2. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    3. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    4. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    5. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    6. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    7. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    8. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    9. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    10. António Couto & Paula Costa & Teresa Simões, 2021. "Identification of Extreme Wind Events Using a Weather Type Classification," Energies, MDPI, vol. 14(13), pages 1-16, July.
    11. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    12. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    13. Cabello-López, Tomás & Carranza-García, Manuel & Riquelme, José C. & García-Gutiérrez, Jorge, 2023. "Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level," Applied Energy, Elsevier, vol. 350(C).
    14. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    15. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    16. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    17. Quan Zhou & Taotao Xiong & Mubin Wang & Chenmeng Xiang & Qingpeng Xu, 2017. "Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS," Energies, MDPI, vol. 10(7), pages 1-15, July.
    18. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.
    19. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
    20. Ricardo Granizo Arrabé & Carlos Antonio Platero Gaona & Fernando Álvarez Gómez & Emilio Rebollo López, 2016. "Novel Auto-Reclosing Blocking Method for Combined Overhead-Cable Lines in Power Networks," Energies, MDPI, vol. 9(11), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:484-:d:310524. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.