Differential Protection for Power Transformers Using Tree-Based Pipeline Optimization Tool

Abstract

Accurate differentiation between Inrush Current (IC) and internal faults (IFs) is crucial for power transformer differential protection. During transformer energization and IC generation, transformer differential protection may misinterpret IC as an internal fault, leading to a trip command being issued to the breakers. This study proposes an innovative machine learning-based approach named the tree-based pipeline optimization tool (TPOT) to enhance the F-score and efficiency of IC detection in relation to IFs in power transformers. TPOT performs in-depth data analysis and extracts significant features that influence the distinction between IC and transformer internal faults. As a model optimizer, TPOT fine-tunes models by adjusting parameters and structures. Consequently, this approach enables differentiation between IC and IFs in power transformers with high F -score and continuous improvements in detection capability. Simulation results on a real 160 MVA, 230/63 kV transformer in the MATLAB and Python software environments demonstrate the effectiveness of the proposed protection scheme in classifying transformer IC from IFs with an F1-score of 92%. © 2025 IEEE.