Machine Learning-Based Health Index Evaluation of Power Transformers Using Novel Parameterization for Predictive Maintenance: Data-Driven Research on Pakistan’s National Grid Regarding Maintenance Cost Optimization

Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R2 Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance.