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For stepping up and down voltage levels, in electrical power infrastructure power transformers play an important role in efficient transmission and distribution of electricity However, transformer failures are a common problem that can result in power outages, equipment damage, and safety risks. The study involved extensive experimentation with various preprocessing techniques, feature selection methods, resampling techniques, and model evaluation metrics to identifying when transformers are likely to fail, this can help prevent transformer failures incidents and minimize their impact. This study focused on developing an effective predictive model to identify transformer failures using the Winding Temperature Indicator (WTI) as the target feature.

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Enhancing Power Transformer Reliability with Machine Learning-Based Fault Detection and Data Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Abha Sharma,
  • Vaasu Bisht,
  • Aarav Rajput,
  • Vansh Dugar,
  • Anand Lahoti

摘要

For stepping up and down voltage levels, in electrical power infrastructure power transformers play an important role in efficient transmission and distribution of electricity However, transformer failures are a common problem that can result in power outages, equipment damage, and safety risks. The study involved extensive experimentation with various preprocessing techniques, feature selection methods, resampling techniques, and model evaluation metrics to identifying when transformers are likely to fail, this can help prevent transformer failures incidents and minimize their impact. This study focused on developing an effective predictive model to identify transformer failures using the Winding Temperature Indicator (WTI) as the target feature.