IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6540-d654118.html
   My bibliography  Save this article

Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

Author

Listed:
  • Mohammed A. Shams

    (Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, Egypt)

  • Hussein I. Anis

    (Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, Egypt)

  • Mohammed El-Shahat

    (Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, Egypt)

Abstract

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

Suggested Citation

  • Mohammed A. Shams & Hussein I. Anis & Mohammed El-Shahat, 2021. "Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform," Energies, MDPI, vol. 14(20), pages 1-22, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6540-:d:654118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6540/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/20/6540/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amir Abbas Soltani & Ayman El-Hag, 2019. "Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network," Energies, MDPI, vol. 12(18), pages 1-14, September.
    2. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
    3. Mohanad S. Al-Musaylh & Ravinesh C. Deo & Yan Li, 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms," Energies, MDPI, vol. 13(9), pages 1-19, May.
    4. Josué M. Polanco-Martínez & Luis M. Abadie, 2016. "Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach," Energies, MDPI, vol. 9(12), pages 1-19, December.
    5. Jansen M. & Bultheel A., 2001. "Empirical Bayes Approach to Improve Wavelet Thresholding for Image Noise Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 629-639, June.
    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. Guo Wang & Yibin Wang & Yongzhi Min & Wu Lei, 2022. "Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis," Energies, MDPI, vol. 15(16), pages 1-15, August.
    2. Linao Li & Xinlao Wei, 2022. "Power Interference Suppression Method for Measuring Partial Discharges under Pulse Square Voltage Conditions," Energies, MDPI, vol. 15(9), pages 1-15, May.
    3. Wenchen Chen & Yingdong Liu & Yayu Gao & Jingzhu Hu & Zhenghai Liao & Jun Zhao, 2024. "Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation," Energies, MDPI, vol. 17(13), pages 1-24, June.
    4. Vu Cong Thuc & Han Soo Lee, 2022. "Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method," Energies, MDPI, vol. 15(16), pages 1-17, August.

    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. Junior, Peterson Owusu & Tiwari, Aviral Kumar & Padhan, Hemachandra & Alagidede, Imhotep, 2020. "Analysis of EEMD-based quantile-in-quantile approach on spot- futures prices of energy and precious metals in India," Resources Policy, Elsevier, vol. 68(C).
    2. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2024. "Volatility spillovers across the spot and futures oil markets after news announcements," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    3. Minh-Tuan Nguyen & Viet-Hung Nguyen & Suk-Jun Yun & Yong-Hwa Kim, 2018. "Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 11(5), pages 1-13, May.
    4. Jiaying Deng & Wenhai Zhang & Xiaomei Yang, 2019. "Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network," Energies, MDPI, vol. 12(10), pages 1-16, May.
    5. Theodosios Perifanis, 2019. "Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods," Energies, MDPI, vol. 12(14), pages 1-16, July.
    6. Haresh Kumar & Muhammad Shafiq & Kimmo Kauhaniemi & Mohammed Elmusrati, 2024. "A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques," Energies, MDPI, vol. 17(5), pages 1-31, February.
    7. Ana C. N. Pardauil & Thiago P. Nascimento & Marcelo R. S. Siqueira & Ubiratan H. Bezerra & Werbeston D. Oliveira, 2020. "Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators," Energies, MDPI, vol. 13(22), pages 1-18, November.
    8. Ben Salem, Leila & Nouira, Ridha & Jeguirim, Khaled & Rault, Christophe, 2022. "The determinants of crude oil prices: Evidence from ARDL and nonlinear ARDL approaches," Resources Policy, Elsevier, vol. 79(C).
    9. Linao Li & Xinlao Wei, 2022. "Power Interference Suppression Method for Measuring Partial Discharges under Pulse Square Voltage Conditions," Energies, MDPI, vol. 15(9), pages 1-15, May.
    10. Polanco Martínez, Josué M. & Abadie, Luis M. & Fernández-Macho, J., 2018. "A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices," Applied Energy, Elsevier, vol. 228(C), pages 1550-1560.
    11. Angeliki Skoura, 2019. "Detection of Lead-Lag Relationships Using Both Time Domain and Time-Frequency Domain; An Application to Wealth-To-Income Ratio," Economies, MDPI, vol. 7(2), pages 1-27, April.
    12. Abdullahi Abubakar Mas’ud & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Nurul Aini Bani, 2017. "Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources," Energies, MDPI, vol. 10(7), pages 1-20, July.
    13. Mobeen Ur Rehman, 2020. "Dynamic correlation pattern amongst alternative energy market for diversification opportunities," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 9(1), pages 1-24, December.
    14. Polanco-Martínez, J.M. & Fernández-Macho, J. & Neumann, M.B. & Faria, S.H., 2018. "A pre-crisis vs. crisis analysis of peripheral EU stock markets by means of wavelet transform and a nonlinear causality test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1211-1227.
    15. Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.
    16. Ju Tang & Miao Jin & Fuping Zeng & Siyuan Zhou & Xiaoxing Zhang & Yi Yang & Yan Ma, 2017. "Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance," Energies, MDPI, vol. 10(10), pages 1-14, October.
    17. Sonia Barrios & David Buldain & María Paz Comech & Ian Gilbert & Iñaki Orue, 2019. "Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress," Energies, MDPI, vol. 12(13), pages 1-16, June.
    18. Ju Tang & Xu Yang & Dong Yang & Qiang Yao & Yulong Miao & Chaohai Zhang & Fuping Zeng, 2017. "Using SF 6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect," Energies, MDPI, vol. 10(8), pages 1-17, August.
    19. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    20. Luis Mª Abadie & José M. Chamorro, 2017. "Valuation of Real Options in Crude Oil Production," Energies, MDPI, vol. 10(8), pages 1-21, August.

    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:14:y:2021:i:20:p:6540-:d:654118. 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.