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Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm

Author

Listed:
  • El-Sayed M. El-kenawy

    (Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt)

  • Fahad Albalawi

    (Electrical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Sayed A. Ward

    (Electrical Engineering Department, Shoubra Faculty of Engineering, Benha University, 108 Shoubra St., Cairo 11629, Egypt
    Faculty of Engineering, Delta University for Science and Technology, Mansoura 11152, Egypt)

  • Sherif S. M. Ghoneim

    (Electrical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Marwa M. Eid

    (Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt)

  • Abdelaziz A. Abdelhamid

    (Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
    Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Nadjem Bailek

    (Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Tamanrasset 11001, Algeria)

  • Abdelhameed Ibrahim

    (Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

Abstract

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers’ ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults’ diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon’s rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases.

Suggested Citation

  • El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3144-:d:904127
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    References listed on IDEAS

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    1. Confalonieri, R. & Bellocchi, G. & Bregaglio, S. & Donatelli, M. & Acutis, M., 2010. "Comparison of sensitivity analysis techniques: A case study with the rice model WARM," Ecological Modelling, Elsevier, vol. 221(16), pages 1897-1906.
    2. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    3. Hazlee Azil Illias & Xin Rui Chai & Ab Halim Abu Bakar & Hazlie Mokhlis, 2015. "Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    4. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
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    Cited by:

    1. Abdelaziz A. Abdelhamid & El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Doaa Sami Khafaga & Amal H. Alharbi & Abdelhameed Ibrahim & Marwa M. Eid & Mohamed Saber, 2022. "Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-29, October.
    2. Christy Jackson Joshua & Prassanna Jayachandran & Abdul Quadir Md & Arun Kumar Sivaraman & Kong Fah Tee, 2023. "Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    3. Abdelaziz A. Abdelhamid & El-Sayed M. El-Kenawy & Fadwa Alrowais & Abdelhameed Ibrahim & Nima Khodadadi & Wei Hong Lim & Nuha Alruwais & Doaa Sami Khafaga, 2022. "Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households," Energies, MDPI, vol. 15(23), pages 1-25, December.
    4. Abdelhameed Ibrahim & El-Sayed M. El-kenawy & A. E. Kabeel & Faten Khalid Karim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Sayed A. Ward & Emad M. S. El-Said & M. El-Said & Doaa Sami Khafaga, 2023. "Al-Biruni Earth Radius Optimization Based Algorithm for Improving Prediction of Hybrid Solar Desalination System," Energies, MDPI, vol. 16(3), pages 1-20, January.
    5. Bonginkosi A. Thango, 2022. "Dissolved Gas Analysis and Application of Artificial Intelligence Technique for Fault Diagnosis in Power Transformers: A South African Case Study," Energies, MDPI, vol. 15(23), pages 1-17, November.

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