Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis

Fettouma Guerbas; Youcef Benmahamed; Youcef Teguar; Rayane Amine Dahmani; Madjid Teguar; Enas Ali; Mohit Bajaj; Shir Ahmad Dost Mohammadi; Sherif S M Ghoneim

doi:10.1038/s41598-024-60071-0

Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis

Sci Rep. 2024 Apr 23;14(1):9271. doi: 10.1038/s41598-024-60071-0.

Authors

Affiliations

¹ Laboratoire des Systèmes Electriques et Industriels, Université des Sciences et de la Technologie Houari Boumediene, BP 32Bab Ezzouar, 16111, Algiers, Algeria.
² Laboratoire de Recherche en Electrotechnique, Ecole Nationale Polytechnique, 10 Rue des Frères Oudek, El Harrach, 16200, Algiers, Algeria.
³ Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
⁴ Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India. mb.czechia@gmail.com.
⁵ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan. mb.czechia@gmail.com.
⁶ Graphic Era Hill University, Dehradun, 248002, India. mb.czechia@gmail.com.
⁷ Department of Electrical and Electronics, Faculty of Engineering, Alberoni University, Kapisa, Afghanistan. sh_ahmad.dm@au.edu.af.
⁸ Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.

Abstract

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Keywords: Artificial neural networks; Diagnosis; Dissolved gas analysis; Electrical and thermal faults; Insulating oil; Optimization; Particle swarm algorithm; Power transformer.