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@article{173933,
        author = {Priya Pralhad Dhule and Priyanka Suresh Pawar and Tanvi Sanjay Girhe and Shital Sopan Gavhale and Snehal Sopan Ingle and Prof. Nilesh G. Bundhe},
        title = {Power Transformer Health Monitoring using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2110-2114},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173933},
        abstract = {It is essential for power transformers, which are crucial parts of electrical networks, to function dependably in order to keep the system stable. It is possible for transformer faults to result in significant disruptions and costly repairs if they are not accurately diagnosed and classified. The purpose of this study is to present a machine learning algorithm for transformer fault classification that makes use of failure history data and advanced pattern recognition algorithms. This approach to transformer operating scenario classification makes use of decision trees, support vector machines, and latent differential equations. In order to improve the accuracy of categorisation, the data is preprocessed. A 5-fold validation validates the performance of the model. The findings demonstrate that machine learning improves predictive maintenance, grid dependability, and transformer health monitoring compared to traditional methods.},
        keywords = {Transformer Fault Diagnosis, Machine Learning, Support Vector Machine.},
        month = {March},
        }