Work

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

Year: 2025

Type: article

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.

Source: High Voltage

DOI: 10.1049/hve2.70093

Authors: Ziwen Huang, Lufen Jia, Wenwen Gu, Weigen Chen, Qu Zhou

Institutions: Southwest University, Chongqing University

Language: English

Topic: Power Transformer Diagnostics and Insulation

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,200