Power transformer health index and life span assessment: a comprehensive review of conventional and machine learning based approaches.

Zahra, Syeda Tahreem; Imdad, Syed Kashif; Khan, Sohail; Khalid, Sohail; Baig, Nauman Anwar

Abstract

Power transformers play a critical role within the electrical power system, making their health assessment and the prediction of their remaining lifespan paramount for the purpose of ensuring efficient operation and facilitating effective maintenance planning. This paper undertakes a comprehensive examination of existent literature, with a primary focus on both conventional and cutting-edge techniques employed within this domain. The merits and demerits of recent methodologies and techniques are subjected to meticulous scrutiny and explication. Furthermore, this paper expounds upon intelligent fault diagnosis methodologies and delves into the most widely utilized intelligent algorithms for the assessment of transformer conditions. Diverse Artificial Intelligence (AI) approaches, including Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), are elucidated offering pragmatic solutions for enhancing the performance of transformer fault diagnosis. The amalgamation of multiple AI methodologies and the exploration of time-series analysis further contribute to the augmentation of diagnostic precision and the early detection of faults in transformers. By furnishing a comprehensive panorama of AI applications in the field of transformer fault diagnosis, this study lays the groundwork for future research endeavors and the progression of this critical area of study.

Citation

ZAHRA, S.T., IMDAD, S.K., KHAN, S., KHALID, S. and BAIG, N.A. 2025. Power transformer health index and life span assessment: a comprehensive review of conventional and machine learning based approaches. Engineering applications of artificial intelligence [online], 139(A), article number 109474. Available from: https://doi.org/10.1016/j.engappai.2024.109474

• Journal Article Type: Article
• Acceptance Date: Oct 11, 2024
• Online Publication Date: Oct 29, 2024
• Publication Date: Jan 31, 2025
• Deposit Date: Oct 30, 2024
• Publicly Available Date: Oct 30, 2024
• Journal: Engineering applications of artificial intelligence
• Print ISSN: 0952-1976
• Electronic ISSN: 1873-6769
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 139
• Issue: A
• Article Number: 109474
• DOI: https://doi.org/10.1016/j.engappai.2024.109474
• Keywords: Power transformers; Fibre-optic sensors; Frequency domain spectroscopy; Random forests; Genetic algorithms; Particle swarm optimisation; Machine learning; Artificial neural networks; Artificial intelligence
• Public URL: https://rgu-repository.worktribe.com/output/2548390