Detection of micro‐water in transformer oil based on ultrasonic pulse‐echo method and sparrow search algorithm‐random forest

Abstract This study proposes a novel transformer oil micro‐water detection method based on the ultrasonic pulse‐echo technique, optimised by a sparrow search algorithm (SSA) to enhance the prediction performance of a random forest (RF) model. Initially, finite element simulations were conducted to select optimal ultrasonic frequencies of 2 and 2.5 MHz. An accelerated thermal ageing experiment was performed using #25 Karamay oil samples, and ultrasonic pulse‐echo signals were collected via a custom‐built detection platform. Variational mode decomposition was employed to extract effective echoes from the raw pulse‐echo signals. Temporal and frequency domain analyses yielded 162 dimensional features, which were subsequently filtered to 88 key parameters using the maximum information coefficient method. A transformer oil micro‐water detection model was then developed by integrating the SSA with RF and trained using K‐fold cross‐validation. The model achieved an impressive average prediction accuracy of 97.34% over 10 cross‐validation runs. The testing set demonstrated a prediction accuracy of 96.40%, a remarkable improvement of 16.53% compared to the unoptimised RF model. The findings provide a solid foundation for the rapid detection of micro‐water content in transformer oil using the ultrasonic pulse‐echo method.