Advanced Health Index Prediction for Transformers Using CatBoost Algorithm
DOI:
https://doi.org/10.48047/tz1n1p66Keywords:
Transformer Health Index, Predictive Maintenance, CatBoost Algorithm, Machine Learning, Dissolved Gas Analysis (DGA).Abstract
The reliability of power transformers is critical for ensuring stable and efficient energy distribution. Traditional transformer maintenance methods often rely on reactive strategies, leading to unexpected failures and costly downtime. This study proposes an advanced health index prediction model using the CatBoost algorithm to enhance predictive maintenance strategies. By leveraging key diagnostic indicators such as dissolved gas analysis (DGA) parameters, oil quality indicators, and electrical properties, the model effectively predicts transformer health status. The research explores various train-test split ratios and hyperparameter tuning to optimize model performance. Results indicate that a 75-25 train-test split yields the best predictive accuracy, with an R² of 0.759
and the lowest RMSE of 9.089. The findings highlight the effectiveness of CatBoost in handling categorical data, improving model interpretability, and reducing overfitting. This approach enables utilities to make proactive maintenance decisions, minimizing unexpected failures and extending the lifespan of transformers.
Downloads
References
M. Islam, G. Lee, S. N. Hettiwatte, and K. Williams, “Calculating a Health Index for Power Transformers Using a Subsystem-Based
GRNN Approach,” IEEE Transactions on Power Delivery, Aug. 2018, doi: 10.1109/TPWRD.2017.2770166.
A. J. Alvares and R. R. Gudwin, “Integrated System of Predictive Maintenance and Operation of Eletronorte Based on Expert System,” IEEE Latin America Transactions, Sep. 2019, doi: 10.1109/TLA.2019.8826707.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
