Santamaria-Bonfil, G.; Arroyo-Figueroa, G.; Zuniga-Garcia, M.A.; Azcarraga Ramos, C.G.; Bassam, A. Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches. Energies2024, 17, 77.
Santamaria-Bonfil, G.; Arroyo-Figueroa, G.; Zuniga-Garcia, M.A.; Azcarraga Ramos, C.G.; Bassam, A. Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches. Energies 2024, 17, 77.
Santamaria-Bonfil, G.; Arroyo-Figueroa, G.; Zuniga-Garcia, M.A.; Azcarraga Ramos, C.G.; Bassam, A. Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches. Energies2024, 17, 77.
Santamaria-Bonfil, G.; Arroyo-Figueroa, G.; Zuniga-Garcia, M.A.; Azcarraga Ramos, C.G.; Bassam, A. Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches. Energies 2024, 17, 77.
Abstract
A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.
Keywords
Transformers Fault Diagnosis; Machine Learning; Automatic Machine Learning; Power Systems
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
