IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p1060-d105524.html
   My bibliography  Save this article

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

Author

Listed:
  • Abdullahi Abubakar Mas’ud

    (Department of Electrical and Electronics Engineering, Jubail Industrial College, P.O. Box 10099, Jubail 31961, Saudi Arabia)

  • Jorge Alfredo Ardila-Rey

    (Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago de Chile 8940000, Chile)

  • Ricardo Albarracín

    (Departamento de Ingeniería Eléctrica, Electrónica, Automática y Física Aplicada, Escuela Técnica Superior de Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain)

  • Firdaus Muhammad-Sukki

    (School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK)

  • Nurul Aini Bani

    (UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia)

Abstract

This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number ( φ-q-n ) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1060-:d:105524
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/1060/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/7/1060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Enas Taha Sayed, 2023. "Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production," Sustainability, MDPI, vol. 15(3), pages 1-12, January.
    2. Habib Benbouhenni & Nicu Bizon, 2021. "Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System," Mathematics, MDPI, vol. 9(19), pages 1-36, September.
    3. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    4. 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.
    5. Vo-Nguyen Tuyet-Doan & Tien-Tung Nguyen & Minh-Tuan Nguyen & Jong-Ho Lee & Yong-Hwa Kim, 2020. "Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 13(8), pages 1-16, April.
    6. Enas Taha Sayed & Hegazy Rezk & Abdul Ghani Olabi & Mohamed R. Gomaa & Yahia B. Hassan & Shek Mohammad Atiqure Rahman & Sheikh Khaleduzzaman Shah & Mohammad Ali Abdelkareem, 2022. "Application of Artificial Intelligence to Improve the Thermal Energy and Exergy of Nanofluid-Based PV Thermal/Nano-Enhanced Phase Change Material," Energies, MDPI, vol. 15(22), pages 1-13, November.
    7. Ahmed Fathy & Hegazy Rezk & Dalia Yousri & Abdullah G. Alharbi & Sulaiman Alshammari & Yahia B. Hassan, 2023. "Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    8. 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.
    9. 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.
    10. Luca Barbieri & Andrea Villa & Roberto Malgesini & Daniele Palladini & Christian Laurano, 2021. "An Innovative Sensor for Cable Joint Monitoring and Partial Discharge Localization," Energies, MDPI, vol. 14(14), pages 1-12, July.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.
    7. 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.
    8. 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.
    9. 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.
    10. Ju Tang & Xu Yang & Gaoxiang Ye & Qiang Yao & Yulong Miao & Fuping Zeng, 2017. "Decomposition Characteristics of SF 6 and Partial Discharge Recognition under Negative DC Conditions," Energies, MDPI, vol. 10(4), pages 1-16, April.
    11. Zhenghai Liao & Dazheng Wang & Liangliang Tang & Jinli Ren & Zhuming Liu, 2017. "A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network," Energies, MDPI, vol. 10(2), pages 1-11, February.
    12. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
    13. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    14. Yi Zhang & Yang Yu & Yingying Zhang & Zehuan Liu & Mingjia Zhang, 2024. "A Semi-Supervised Approach for Partial Discharge Recognition Combining Graph Convolutional Network and Virtual Adversarial Training," Energies, MDPI, vol. 17(18), pages 1-15, September.

    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:10:y:2017:i:7:p:1060-:d:105524. 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.