IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7656-d944586.html
   My bibliography  Save this article

Contamination Level Monitoring Techniques for High-Voltage Insulators: A Review

Author

Listed:
  • Luqman Maraaba

    (Department of Electrical Engineering, Arab American University, 13 Zababdeh, Jenin P.O. Box 240, Palestine)

  • Khaled Al-Soufi

    (Applied Research Center for Metrology, Standards and Testing, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Twaha Ssennoga

    (Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

  • Azhar M. Memon

    (Applied Research Center for Metrology, Standards and Testing, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Muhammed Y. Worku

    (Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Luai M. Alhems

    (Applied Research Center for Metrology, Standards and Testing, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7656-:d:944586
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7656/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/20/7656/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lijun Jin & Da Zhang, 2015. "Contamination Grades Recognition of Ceramic Insulators Using Fused Features of Infrared and Ultraviolet Images," Energies, MDPI, vol. 8(2), pages 1-22, January.
    2. Lucas de Paula Santos Petri & Emanuel Antonio Moutinho & Rondinele Pinheiro Silva & Renato Massoni Capelini & Rogério Salustiano & Guilherme Martinez Figueiredo Ferraz & Estácio Tavares Wanderley Neto, 2020. "A Portable System for the Evaluation of the Degree of Pollution of Transmission Line Insulators," Energies, MDPI, vol. 13(24), pages 1-26, December.
    3. Ang Ren & Qingquan Li & Huaishuo Xiao, 2017. "Influence Analysis and Prediction of ESDD and NSDD Based on Random Forests," Energies, MDPI, vol. 10(7), pages 1-19, June.
    4. Mohammed El Amine Slama & Adnan Krzma & Maurizio Albano & Abderrahmane Manu Haddad, 2022. "Experimental Study and Modeling of the Effect of ESDD/NSDD on AC Flashover of SiR Outdoor Insulators," Energies, MDPI, vol. 15(10), pages 1-14, May.
    5. Arshad & Jawad Ahmad & Ahsen Tahir & Brian G. Stewart & Azam Nekahi, 2020. "Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique," Energies, MDPI, vol. 13(15), pages 1-16, July.
    6. SK Manirul Haque & Jorge Alfredo Ardila-Rey & Yunusa Umar & Abdullahi Abubakar Mas’ud & Firdaus Muhammad-Sukki & Binta Hadi Jume & Habibur Rahman & Nurul Aini Bani, 2021. "Application and Suitability of Polymeric Materials as Insulators in Electrical Equipment," Energies, MDPI, vol. 14(10), pages 1-29, May.
    7. Saiful Mohammad Iezham Suhaimi & Nouruddeen Bashir & Nor Asiah Muhamad & Nurun Najah Abdul Rahim & Noor Azlinda Ahmad & Mohd Nazri Abdul Rahman, 2019. "Surface Discharge Analysis of High Voltage Glass Insulators Using Ultraviolet Pulse Voltage," Energies, MDPI, vol. 12(2), pages 1-26, January.
    8. Da Zhang & Shuailin Chen, 2020. "Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information," Energies, MDPI, vol. 13(19), pages 1-16, October.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Wen Si & Simeng Li & Huaishuo Xiao & Qingquan Li & Yalin Shi & Tongqiao Zhang, 2018. "Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer," Energies, MDPI, vol. 11(3), pages 1-19, March.
    3. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    4. Dongdong Zhang & Hong Xu & Jin Liu & Chengshun Yang & Xiaoning Huang & Zhijin Zhang & Xingliang Jiang, 2021. "Research on the Non-Contact Pollution Monitoring Method of Composite Insulator Based on Space Electric Field," Energies, MDPI, vol. 14(8), pages 1-15, April.
    5. 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.
    6. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    7. Issouf Fofana & Stephan Brettschneider, 2022. "Outdoor Insulation and Gas-Insulated Switchgears," Energies, MDPI, vol. 15(21), pages 1-7, November.
    8. 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.
    9. Jiazheng Lu & Jianping Hu & Zhen Fang & Xinhan Qiao & Zhijin Zhang, 2021. "Electric Field Distribution and AC Breakdown Characteristics of Polluted Novel Lightning Protection Insulator under Icing Conditions," Energies, MDPI, vol. 14(22), pages 1-11, November.
    10. Zhijin Zhang & Hang Zhang & Song Yue & Hao Wang, 2023. "Contamination Deposit and Model of Insulator," Energies, MDPI, vol. 16(6), pages 1-3, March.
    11. 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.
    12. Irfan Ullah & Rehan Ullah Khan & Fan Yang & Lunchakorn Wuttisittikulkij, 2020. "Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment," Energies, MDPI, vol. 13(2), pages 1-17, January.
    13. 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.
    14. 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.
    15. Ang Ren & Hongshun Liu & Jianchun Wei & Qingquan Li, 2017. "Natural Contamination and Surface Flashover on Silicone Rubber Surface under Haze–Fog Environment," Energies, MDPI, vol. 10(10), pages 1-18, October.
    16. Ning Guo & Jiaming Sun & Yunlei Li & Xiaoyu Lv & Junguo Gao & Mingpeng He & Yue Zhang, 2022. "Nonlinear Surface Conductivity Characteristics of Epoxy Resin-Based Micro-Nano Structured Composites," Energies, MDPI, vol. 15(15), pages 1-15, July.
    17. 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.
    18. Saiful Mohammad Iezham Suhaimi & Nouruddeen Bashir & Nor Asiah Muhamad & Nurun Najah Abdul Rahim & Noor Azlinda Ahmad & Mohd Nazri Abdul Rahman, 2019. "Surface Discharge Analysis of High Voltage Glass Insulators Using Ultraviolet Pulse Voltage," Energies, MDPI, vol. 12(2), pages 1-26, January.

    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:15:y:2022:i:20:p:7656-:d:944586. 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.