Summary of Supervised Learning Based Method For Condition Monitoring Of Overhead Line Insulators Using Leakage Current Measurement, by Mile Mitrovic et al.
Supervised Learning based Method for Condition Monitoring of Overhead Line Insulators using Leakage Current Measurement
by Mile Mitrovic, Dmitry Titov, Klim Volkhov, Irina Lukicheva, Andrey Kudryavzev, Petr Vorobev, Qi Li, Vladimir Terzija
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel machine learning-based approach to estimate the flashover probability of cup-and-pin glass insulator strings in overhead power lines. The proposed method uses Extreme Gradient Boosting (XGBoost) with leakage current and applied voltage as inputs to predict critical flashover voltages for various insulator designs and voltage levels. This risk-based approach aims to revolutionize asset management in the power grid by accurately determining the condition of insulator strings and guiding maintenance decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in the power grid: figuring out when to replace insulators on overhead power lines. Right now, it’s hard to predict when an insulator will fail, which can cause power outages. The researchers developed a new machine learning method that uses special data about leakage current and voltage to estimate the risk of flashovers happening. This helps power grid companies make better decisions about how to maintain their equipment. |
Keywords
» Artificial intelligence » Extreme gradient boosting » Machine learning » Probability » Xgboost
