Power Transformer Health Index and Life Span Assessment: A Comprehensive Review of Conventional and Machine Learning based Approaches
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| Format: | Preprint |
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2025
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| _version_ | 1866916701435592704 |
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| author | Zahra, Syeda Tahreem Imdad, Syed Kashif Khan, Sohail Khalid, Sohail Baig, Nauman Anwar |
| author_facet | Zahra, Syeda Tahreem Imdad, Syed Kashif Khan, Sohail Khalid, Sohail Baig, Nauman Anwar |
| contents | 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 timeseries 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_15310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Machine Learning Artificial Intelligence 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 timeseries 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. |
| title | Power Transformer Health Index and Life Span Assessment: A Comprehensive Review of Conventional and Machine Learning based Approaches |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15310 |