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

El-Sayed M. El-kenawy, Fahad Albalawi, Sayed A. Ward, Sherif S. M. Ghoneim, Marwa M. Eid, Abdelaziz A. Abdelhamid, Nadjem Bailek and Abdelhameed Ibrahim ()
Additional contact information
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
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
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

Mathematics, 2022, vol. 10, issue 17, 1-28

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.

Keywords: diagnostic accuracy; transformer faults; Gravitational Search; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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