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Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

Author

Listed:
  • Abdullahi Abubakar Mas’ud

    (Department of Electrical and Electronics Engineering, Jubail Industrial College, Jubail 10099, Saudi Arabia)

  • Ricardo Albarracín

    (Department of Electrical, Electronics and Automation Engineering and Applied Physics, Universidad Politécnica de Madrid, Ronda de Valencia 3, Madrid 28012, Spain)

  • Jorge Alfredo Ardila-Rey

    (Department of Electrical Engineering, Federico Santa María Technical University, Santiago de Chile 8940000, Chile)

  • Firdaus Muhammad-Sukki

    (School of Engineering, Faculty of Design and Technology, Robert Gordon University, Aberdeen AB10 7GJ, UK)

  • Hazlee Azil Illias

    (Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Nurul Aini Bani

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

  • Abu Bakar Munir

    (Faculty of Law, University of Malaya, Kuala Lumpur 50603, Malaysia
    University of Malaya Malaysian Centre of Regulatory Studies (UMCoRS), University of Malaya, Jalan Pantai Baru, Kuala Lumpur 59990, Malaysia)

Abstract

In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:574-:d:74630
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    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. 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.
    9. 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.
    10. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.

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