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@article{171195,
        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 = {TRANSFORMER FAULT DIAGNOSIS TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3019-3024},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171195},
        abstract = {The efficient transmission and distribution of electricity, as well as the overall operation of the power system, are dependent on the transformer. The study lays out the history of transformer failure detection methods and gives an overview of them. A large number of academics have sought to improve upon these time-honoured techniques by using smart technologies like support vector machines, neural networks, and machine learning. A new approach and technology for the safe and dependable operation and routine maintenance of power systems are provided by combining transformer fault prediction with a machine learning algorithm. This helps maintenance personnel of power systems to accurately predict the running state of power equipment.},
        keywords = {Transformer Fault Diagnosis, Machine Learning, Support Vector Machine.},
        month = {December},
        }